Inequalities in the impact of COVID-19-associated disruptions on tuberculosis diagnosis by age and sex in 45 high TB burden countries

1. Background and required packages

We set the working file directory and load all required R packages for analysis into R.

2. Loading data

We load the required data and select countries of interest

# High TB burden country lists
country_HBC<-c('Angola','Bangladesh','Brazil','Central African Republic','China','Congo','Democratic People\'s Republic of Korea','Democratic Republic of the Congo','Ethiopia','Gabon','India','Indonesia','Kenya','Lesotho','Liberia','Mongolia','Mozambique','Myanmar','Namibia','Nigeria','Pakistan','Papua New Guinea','Philippines','Sierra Leone','South Africa','Thailand','Uganda','United Republic of Tanzania','Viet Nam','Zambia')
country_MDR<-c('Angola','Azerbaijan','Bangladesh','Belarus','China','Democratic People\'s Republic of Korea','Democratic Republic of the Congo','India','Indonesia','Kazakhstan','Kyrgyzstan','Mongolia','Mozambique','Myanmar','Nepal','Nigeria','Pakistan','Papua New Guinea','Peru','Philippines','Republic of Moldova','Russian Federation','Somalia','South Africa','Tajikistan','Ukraine','Uzbekistan','Viet Nam','Zambia','Zimbabwe')
country_HIV<-c('Botswana','Brazil','Cameroon','Central African Republic','China','Congo','Democratic Republic of the Congo','Eswatini','Ethiopia','Gabon','Guinea','Guinea-Bissau','India','Indonesia','Kenya','Lesotho','Liberia','Malawi','Mozambique','Myanmar','Namibia','Nigeria','Philippines','Russian Federation','South Africa','Thailand','Uganda','United Republic of Tanzania','Zambia','Zimbabwe')
country_list<-unique(c(country_HBC,country_MDR,country_HIV))
# Remove Mozambique & Uganda as incomplete age data, Angola and Papua New Guinea as only two data points prior to 2020
country_list<-country_list[!country_list %in% c("Angola","Mozambique","Papua New Guinea","Uganda")]
# Read in WHO notification data from https://www.who.int/teams/global-tuberculosis-programme/data
WHO_not<-fread('TB_notifications_2022-06-15.csv')
# Use only HBC countries
WHO_not$g_whoregion<-mgsub(WHO_not$g_whoregion,c("AFR","AMR","EMR","EUR","SEA","WPR"),c("African Region","Region of the Americas","Eastern Mediterranean Region","European Region","South-East Asia Region","Western Pacific Region"))
WHO_not<-WHO_not[country%in%country_list]
# Remove years where new cases only (not new + relapse) are recorded
# Except Azerbaijan where this is always the case, and Mongolia where there is a blank for 2020
WHO_not[country=="Azerbaijan",rel_in_agesex_flg:=1]
WHO_not[country=="Mongolia",rel_in_agesex_flg:=1]
WHO_not<-WHO_not[rel_in_agesex_flg==1]   
WHO_not<-WHO_not[year%in%c(2013:2020)]
# Define categories
WHO_not[,men:=newrel_m15plus]
WHO_not[,women:=newrel_f15plus]
WHO_not[,children:=newrel_m014+newrel_f014]
WHO_not[,adults:=newrel_m1524+newrel_m2534+newrel_m3544+newrel_m4554+newrel_m5564+newrel_f1524+newrel_f2534+newrel_f3544+newrel_f4554+newrel_f5564]
WHO_not[,elderly:=newrel_m65+newrel_f65]
# Simplify data set
dat<-select(WHO_not,country,iso3,g_whoregion,year,men,women,children,adults,elderly)
options(knitr.kable.NA = '')
# Show data
dat %>%
  dplyr::select(!c(country)) %>%
  knitr::kable(col.names=c("Country code","Region","Year","Men (observed)","Women (observed)","Children (observed)","Adults (observed)","Elderly (observed)")) %>%
  kableExtra::kable_styling(full_width = FALSE)

Country code	Region	Year	Men (observed)	Women (observed)	Children (observed)	Adults (observed)	Elderly (observed)
AZE	European Region	2013	3145	1168	215	4114	199
AZE	European Region	2014	3107	1103	179	4010	200
AZE	European Region	2015	2656	1154	179	3599	211
AZE	European Region	2016	2470	1148	175	3436	182
AZE	European Region	2017	2440	1265	166	3473	232
AZE	European Region	2018	2382	1180	200	3305	257
AZE	European Region	2019	2307	1127	179	3166	268
AZE	European Region	2020	1610	857	91	2282	185
BGD	South-East Asia Region	2015	118612	80222	8073	169345	29489
BGD	South-East Asia Region	2016	125646	87336	9266	179283	33699
BGD	South-East Asia Region	2017	134651	97817	10171	193871	38597
BGD	South-East Asia Region	2018	147490	108351	11302	211390	44451
BGD	South-East Asia Region	2019	159498	119767	12330	228291	50974
BGD	South-East Asia Region	2020	123451	97264	9363	181071	39644
BLR	European Region	2013	3232	1224	14	3974	482
BLR	European Region	2014	2796	1038	24	3404	430
BLR	European Region	2015	2740	1007	18	3250	497
BLR	European Region	2016	2291	907	13	2778	420
BLR	European Region	2017	2007	761	13	2386	382
BLR	European Region	2018	1729	618	12	2000	347
BLR	European Region	2019	1603	596	8	1853	346
BLR	European Region	2020	1121	389	4	1310	200
BWA	African Region	2013	3613	2665	556	5783	495
BWA	African Region	2014	3274	2324	419	5133	465
BWA	African Region	2015	2662	2014	296	4327	349
BWA	African Region	2016	2690	1825	288	4097	418
BWA	African Region	2017	2205	1573	319	3427	351
BWA	African Region	2018	1995	1426	229	3058	363
BWA	African Region	2019	1703	1154	232	2523	334
BWA	African Region	2020	1258	811	160	1845	224
BRA	Region of the Americas	2013	50540	24365	2501	67682	7223
BRA	Region of the Americas	2014	50072	23641	2233	66628	7085
BRA	Region of the Americas	2015	50669	22793	2097	66145	7317
BRA	Region of the Americas	2016	51209	22857	2173	66553	7513
BRA	Region of the Americas	2017	53862	22990	2264	68891	7961
BRA	Region of the Americas	2018	57314	24489	2547	73560	8243
BRA	Region of the Americas	2019	57597	24839	2681	73833	8603
BRA	Region of the Americas	2020	49967	21522	1944	64037	7452
CMR	African Region	2016	14334	9792	1401	22794	1332
CMR	African Region	2017	13868	9309	1350	21696	1481
CMR	African Region	2018	13520	8705	1164	20776	1449
CMR	African Region	2019	14209	8855	1255	21496	1568
CMR	African Region	2020	13194	7750	1158	19432	1512
CAF	African Region	2016	5497	3703	1418	8847	353
CAF	African Region	2017	4723	3516	1580	7913	326
CAF	African Region	2018	5246	3888	1861	8749	385
CAF	African Region	2019	5919	4492	1648	9995	416
CAF	African Region	2020	6263	4643	1629	10419	487
CHN	Western Pacific Region	2013	583598	258748	4830	670432	171914
CHN	Western Pacific Region	2014	566364	248755	4164	643948	171171
CHN	Western Pacific Region	2015	550996	243245	4198	618127	176114
CHN	Western Pacific Region	2016	535618	238185	4690	599657	174146
CHN	Western Pacific Region	2017	533295	234862	4993	587319	180838
CHN	Western Pacific Region	2018	541876	246948	6421	591395	197429
CHN	Western Pacific Region	2019	497696	223913	6656	537497	184112
CHN	Western Pacific Region	2020	423296	194405	7014	458615	159086
COG	African Region	2015	4388	3465	783	7326	527
COG	African Region	2016	5294	4037	1093	8669	662
COG	African Region	2017	5126	4002	877	8489	639
COG	African Region	2018	5530	4256	920	9093	693
COG	African Region	2019	6269	4565	968	10048	786
COG	African Region	2020	5902	4376	897	9419	859
PRK	South-East Asia Region	2017	59817	35525	5211	90526	4816
PRK	South-East Asia Region	2018	54936	30250	4753	81369	3817
PRK	South-East Asia Region	2019	59039	32057	4626	87057	4039
PRK	South-East Asia Region	2020	54238	30786	4616	81199	3825
COD	African Region	2016	64101	48684	14213	105880	6905
COD	African Region	2017	75123	57708	16933	123590	9241
COD	African Region	2018	85290	65908	18453	140840	10358
COD	African Region	2020	101798	76418	22340	165082	13134
SWZ	African Region	2013	3318	2702	671	5713	307
SWZ	African Region	2014	2830	2251	502	4831	250
SWZ	African Region	2015	2473	1797	297	4024	246
SWZ	African Region	2016	2065	1524	217	3393	196
SWZ	African Region	2017	1833	1356	178	3015	174
SWZ	African Region	2018	1622	1204	161	2679	147
SWZ	African Region	2019	1580	1120	176	2574	126
SWZ	African Region	2020	1258	783	99	1965	76
ETH	African Region	2015	64509	51722	18444	110185	6046
ETH	African Region	2016	61198	48956	15100	104790	5364
ETH	African Region	2017	58941	44800	12810	98316	5425
ETH	African Region	2018	56517	44866	12053	95946	5437
ETH	African Region	2019	56092	43923	11024	94677	5338
ETH	African Region	2020	54956	42398	10839	92146	5208
GAB	African Region	2014	2955	2190	463	4758	387
GAB	African Region	2015	3166	2034	483	4795	405
GAB	African Region	2016	3127	2060	380	4799	388
GAB	African Region	2018	3339	2059	291	5128	270
GAB	African Region	2019	3138	1894	367	4783	249
GAB	African Region	2020	2861	1777	241	4458	180
GIN	African Region	2013	6993	4135	185	10467	661
GIN	African Region	2015	8231	3730	193	11562	399
GIN	African Region	2016	7715	4259	665	11411	563
GIN	African Region	2017	8043	4808	858	12133	718
GIN	African Region	2018	8363	4964	923	12490	837
GIN	African Region	2019	9599	5634	1157	14156	1077
GIN	African Region	2020	9066	5601	945	13632	1035
GNB	African Region	2014	1348	823	108	2083	88
GNB	African Region	2016	1373	760	90	2034	99
GNB	African Region	2017	1293	808	125	2007	94
GNB	African Region	2018	1233	655	137	1802	86
GNB	African Region	2019	1432	856	119	2189	99
GNB	African Region	2020	1518	903	122	2340	81
IND	South-East Asia Region	2013
IND	South-East Asia Region	2014	1010620	503218	95709	1380532	133306
IND	South-East Asia Region	2015	1046780	521223	99133	1429927	138076
IND	South-East Asia Region	2016	1107520	551470	104886	1512902	146088
IND	South-East Asia Region	2017	1020782	527742	101170	1450134	98390
IND	South-East Asia Region	2018	1142605	641240	124839	1608054	175791
IND	South-East Asia Region	2019	1272074	744675	145574	1805547	211202
IND	South-East Asia Region	2020	949552	582210	97539	1383074	148688
IDN	South-East Asia Region	2013	175558	123970	26054	276940	22588
IDN	South-East Asia Region	2014	177044	122592	23170	274303	25333
IDN	South-East Asia Region	2015	178106	122377	28412	272316	28167
IDN	South-East Asia Region	2016	192516	133490	32602	294192	31814
IDN	South-East Asia Region	2017	226819	158867	52944	344402	41284
IDN	South-East Asia Region	2018	288166	208253	68550	439922	56497
IDN	South-East Asia Region	2019	284339	203886	70040	433137	55088
IDN	South-East Asia Region	2020	203415	145149	35461	313183	35381
KAZ	European Region	2013	11604	6843	511	17242	1205
KAZ	European Region	2014	9000	5792	452	13795	997
KAZ	European Region	2015	8314	5300	392	12469	1145
KAZ	European Region	2016	7463	4533	326	10921	1075
KAZ	European Region	2017	7342	4703	404	10797	1248
KAZ	European Region	2018	7655	4815	362	11096	1374
KAZ	European Region	2019	7396	4754	351	10729	1421
KAZ	European Region	2020	4756	4544	303	8155	1145
KEN	African Region	2014	49810	31036	8448	76448	4398
KEN	African Region	2016	44799	24865	6671	65866	3798
KEN	African Region	2017	49308	26643	7648	71213	4738
KEN	African Region	2018	54719	29847	9968	78022	6544
KEN	African Region	2019	50008	26038	8299	70579	5467
KEN	African Region	2020	44034	22006	5606	61285	4755
KGZ	European Region	2015	3814	2676	537	5966	524
KGZ	European Region	2016	3808	2811	407	5965	654
KGZ	European Region	2017	3625	2626	436	5541	710
KGZ	European Region	2018	3586	2442	310	5277	751
KGZ	European Region	2019	3384	2449	305	5028	805
KGZ	European Region	2020	2370	1691	180	3480	581
LSO	African Region	2014	4803	3543	368	7740	606
LSO	African Region	2015	4469	2710	244	6581	598
LSO	African Region	2016	4199	2538	281	6075	662
LSO	African Region	2017	4324	2413	380	5943	794
LSO	African Region	2018	4361	2406	260	5842	925
LSO	African Region	2019	4373	2402	284	5678	1097
LSO	African Region	2020	2930	1444	191	3607	767
LBR	African Region	2013	2277	1437	121	3552	162
LBR	African Region	2016	4293	2015	872	6132	176
LBR	African Region	2018	3724	2858	1216	6146	436
LBR	African Region	2019	3846	3022	1413	6398	470
LBR	African Region	2020	3550	2349	1056	5569	330
MWI	African Region	2013	8383	6585	1827	13888	1080
MWI	African Region	2014	8724	5716	1827	13357	1083
MWI	African Region	2015	8576	5243	1562	12830	989
MWI	African Region	2016	8553	5327	1374	12903	977
MWI	African Region	2017	9283	5528	1701	13546	1265
MWI	African Region	2018	8837	5396	1399	12839	1394
MWI	African Region	2019	9431	5944	1527	13733	1642
MWI	African Region	2020	8468	5270	1395	12316	1422
MNG	Western Pacific Region	2013	2255	1717	359	3788	184
MNG	Western Pacific Region	2014	2361	1733	389	3894	200
MNG	Western Pacific Region	2015	2429	1832	424	4029	232
MNG	Western Pacific Region	2016	2152	1754	519	3696	210
MNG	Western Pacific Region	2017	2142	1651	427	3578	215
MNG	Western Pacific Region	2018	2052	1533	295	3367	218
MNG	Western Pacific Region	2019	2235	1636	406	3646	225
MNG	Western Pacific Region	2020	1908	1498	455	3198	208
MMR	South-East Asia Region	2014	65260	36727	36301	89237	12750
MMR	South-East Asia Region	2015	66193	37282	34930	90262	13213
MMR	South-East Asia Region	2016	67911	37977	31633	91649	14239
MMR	South-East Asia Region	2017	65783	35908	28723	87626	14065
MMR	South-East Asia Region	2018	71961	39740	26262	95308	16393
MMR	South-East Asia Region	2019	71566	39208	23703	93884	16890
MMR	South-East Asia Region	2020	58384	31724	13244	77017	13091
NAM	African Region	2013	4936	3402	1094	7827	511
NAM	African Region	2016	4710	3306	841	7433	583
NAM	African Region	2017	4658	3105	812	7188	575
NAM	African Region	2018	4430	2665	713	6630	465
NAM	African Region	2019	4406	2662	733	6561	507
NAM	African Region	2020	3684	2181	644	5435	430
NPL	South-East Asia Region	2015	18624	10255	2106	25099	3780
NPL	South-East Asia Region	2016	18367	10197	1776	24709	3855
NPL	South-East Asia Region	2017	18706	10400	1785	24543	4563
NPL	South-East Asia Region	2018	19216	10880	1625	25018	5078
NPL	South-East Asia Region	2019	18964	10710	1723	24040	5634
NPL	South-East Asia Region	2020	15950	9221	1619	20567	4604
NGA	African Region	2013	56513	38112	5776	87935	6690
NGA	African Region	2014	52028	33863	5463	79889	6002
NGA	African Region	2015	53131	32680	4773	79658	6153
NGA	African Region	2016	57163	34872	5244	85230	6805
NGA	African Region	2017	60115	35022	7250	88186	6951
NGA	African Region	2018	60238	35512	8171	88250	7500
NGA	African Region	2019	66842	40845	9462	98550	9137
NGA	African Region	2020	77890	49417	8349	116084	11223
PAK	Eastern Mediterranean Region	2014	141052	140120	27245	252774	28398
PAK	Eastern Mediterranean Region	2015	145515	143382	34370	258425	30472
PAK	Eastern Mediterranean Region	2016	160044	154588	41758	279189	35443
PAK	Eastern Mediterranean Region	2017	162517	152038	44373	276395	38160
PAK	Eastern Mediterranean Region	2018	162451	149764	47804	273128	39087
PAK	Eastern Mediterranean Region	2019	148072	134450	45447	244869	37653
PAK	Eastern Mediterranean Region	2020	123720	112219	37051	206818	29121
PER	Region of the Americas	2015	17421	10853	1559	24856	3418
PER	Region of the Americas	2016	16685	11464	1584	24567	3582
PER	Region of the Americas	2017	17587	10736	1517	24839	3484
PER	Region of the Americas	2018	19571	10521	1329	26045	4047
PER	Region of the Americas	2019	19458	10521	1363	25931	4048
PER	Region of the Americas	2020	14605	7952	933	19889	2668
PHL	Western Pacific Region	2014	32479	14486	12191	42634	4331
PHL	Western Pacific Region	2015	154050	78808	32813	197972	34886
PHL	Western Pacific Region	2016	188226	96016	48699	239847	44395
PHL	Western Pacific Region	2017	184405	93626	39235	235151	42880
PHL	Western Pacific Region	2018	214771	111467	45402	271039	55199
PHL	Western Pacific Region	2019	240296	126178	42669	302963	63511
PHL	Western Pacific Region	2020	161117	76964	18449	199643	38438
MDA	European Region	2013	3174	1177	134	4103	248
MDA	European Region	2014	2845	1099	114	3695	249
MDA	European Region	2015	2568	926	114	3276	218
MDA	European Region	2016	2523	945	103	3206	262
MDA	European Region	2017	2385	850	123	2981	254
MDA	European Region	2018	2180	747	95	2702	225
MDA	European Region	2019	1997	711	101	2456	252
MDA	European Region	2020	1309	402	56	1567	144
RUS	European Region	2014	69153	29280	3195	92327	6106
RUS	European Region	2015	67280	28446	3061	89443	6283
RUS	European Region	2016	62462	26672	2876	83289	5845
RUS	European Region	2017	57261	24272	2494	75763	5770
RUS	European Region	2018	53180	22591	2169	70105	5666
RUS	European Region	2019	49853	20874	2028	65187	5540
RUS	European Region	2020	39917	16774	1627	52191	4500
SLE	African Region	2013	4558	2729	103	6926	361
SLE	African Region	2014	4635	2683	135	6988	330
SLE	African Region	2018	9104	5674	2365	13757	1021
SLE	African Region	2019	9344	6095	2350	14283	1156
SLE	African Region	2020	8668	5489	1547	13198	959
SOM	Eastern Mediterranean Region	2014	6196	3924	2783	9029	1091
SOM	Eastern Mediterranean Region	2015	6675	4151	3156	9660	1166
SOM	Eastern Mediterranean Region	2016	6701	4099	3370	9637	1163
SOM	Eastern Mediterranean Region	2017	7664	4742	4079	11164	1242
SOM	Eastern Mediterranean Region	2018	7757	5173	3684	11713	1217
SOM	Eastern Mediterranean Region	2019	7898	5560	3460	12319	1139
SOM	Eastern Mediterranean Region	2020	7802	5709	3377	12236	1275
ZAF	African Region	2013	154786	120923	36671	263398	12311
ZAF	African Region	2014	157748	116441	31977	261379	12810
ZAF	African Region	2015	150761	107324	29137	245795	12290
ZAF	African Region	2016	128842	87642	20546	205789	10695
ZAF	African Region	2017	122945	81590	15628	193646	10889
ZAF	African Region	2018	126936	83464	17561	198634	11766
ZAF	African Region	2019	112911	80172	16461	181020	12063
ZAF	African Region	2020	106181	71335	13558	173830	3686
TJK	European Region	2014	3028	2445	334	5024	449
TJK	European Region	2015	3023	2543	328	5089	477
TJK	European Region	2016	3070	2506	389	5103	473
TJK	European Region	2017	3027	2515	353	5056	486
TJK	European Region	2018	2932	2448	346	4853	527
TJK	European Region	2019	3038	2313	404	4828	523
TJK	European Region	2020	2068	1840	240	3501	407
THA	South-East Asia Region	2015	22441	9110	109
THA	South-East Asia Region	2016	46870	22324	811	53113	16081
THA	South-East Asia Region	2017	50810	23493	759	57240	17063
THA	South-East Asia Region	2018	56221	25858	840	62469	19610
THA	South-East Asia Region	2019	57942	27480	874	65410	20012
THA	South-East Asia Region	2020	56707	27124	885	63197	20634
UKR	European Region	2013	23317	10189	638	30830	2676
UKR	European Region	2014	22245	8924	532	28699	2470
UKR	European Region	2015	21210	8373	568	27220	2363
UKR	European Region	2016	20807	7674	571	26185	2296
UKR	European Region	2017	18734	7884	611	24296	2322
UKR	European Region	2018	18326	7606	580	23742	2190
UKR	European Region	2019	17573	7221	585	22736	2058
UKR	European Region	2020	12150	5001	382	15747	1404
TZA	African Region	2014	33213	21895	6463	49490	5618
TZA	African Region	2015	34156	21036	5703	49282	5910
TZA	African Region	2016	36162	21973	6474	51700	6435
TZA	African Region	2017	36216	23238	8819	52006	7448
TZA	African Region	2018	39642	24538	10512	55633	8547
TZA	African Region	2019	42576	26392	12240	58626	10342
TZA	African Region	2020	43439	27762	13590	59574	11627
UZB	European Region	2013	11492	7360	1960	16588	2264
UZB	European Region	2014	9471	6961	1913	14195	2237
UZB	European Region	2015	8435	6085	1795	12563	1957
UZB	European Region	2016	7849	6191	2010	11931	2109
UZB	European Region	2017	8428	6425	1989	12654	2199
UZB	European Region	2018	7852	6532	2029	12115	2269
UZB	European Region	2019	7752	6330	2190	11701	2381
UZB	European Region	2020	5696	4682	1733	8591	1787
VNM	Western Pacific Region	2016	76979	23275	1691	83925	16329
VNM	Western Pacific Region	2017	73065	27817	1716	82332	18550
VNM	Western Pacific Region	2018	71043	26859	1656	79965	17937
VNM	Western Pacific Region	2019	72500	28216	1704	82384	18332
VNM	Western Pacific Region	2020	70754	27651	1396	79404	19001
ZMB	African Region	2013	22502	14982	3154	35703	1781
ZMB	African Region	2014	20472	13024	2726	31822	1674
ZMB	African Region	2015	21762	12703	2276	32847	1618
ZMB	African Region	2016	23197	12960	2169	34670	1487
ZMB	African Region	2017	22051	11820	2139	32193	1678
ZMB	African Region	2018	21759	11106	2206	31026	1839
ZMB	African Region	2019	22118	11559	2473	31607	2070
ZMB	African Region	2020	24602	12674	2724	34026	3250
ZWE	African Region	2013	17099	13233	2567	28399	1933
ZWE	African Region	2014	15723	11640	2290	25567	1796
ZWE	African Region	2015	14511	10659	1820	23514	1656
ZWE	African Region	2016	15049	10039	1530	23371	1717
ZWE	African Region	2017	14965	9484	1399	22614	1835
ZWE	African Region	2018	14554	9133	1517	21559	2128
ZWE	African Region	2019	12682	7155	1171	18226	1611
ZWE	African Region	2020	9622	5194	912	13690	1126

# Total notifications
#sum(WHO_not[year%in%c(2013:2019),men],na.rm=TRUE)
#sum(WHO_not[year%in%c(2013:2019),women],na.rm=TRUE)
#sum(WHO_not[year%in%c(2013:2019),children],na.rm=TRUE)
#sum(WHO_not[year%in%c(2013:2019),adults],na.rm=TRUE)
#sum(WHO_not[year%in%c(2013:2019),elderly],na.rm=TRUE)

#sum(WHO_not[year%in%c(2020),men],na.rm=TRUE)
#sum(WHO_not[year%in%c(2020),women],na.rm=TRUE)
#sum(WHO_not[year%in%c(2020),children],na.rm=TRUE)
#sum(WHO_not[year%in%c(2020),adults],na.rm=TRUE)
#sum(WHO_not[year%in%c(2020),elderly],na.rm=TRUE)

3. Linear models

We run country-specific linear models

mod<-list()
gof<-list()
countries<-unique(dat$iso3)
gr<-expand.grid(countries,2013:2020)
datPred<-data.frame(iso3=gr[,1],year=gr[,2],men=NA,women=NA,children=NA,adults=NA,elderly=NA,men_SE=NA,women_SE=NA,children_SE=NA,adults_SE=NA,elderly_SE=NA)
for(c in countries){
  for(var in c("men","women","children","adults","elderly")){
    mod[[paste(sep="_",c,var)]]<-glm(as.formula(paste(sep="",var," ~ year")), data=dat %>% dplyr::filter(iso3==c & year<2020),family=poisson)
    gof[[paste(sep="_",c,var)]]<-1-mod[[paste(sep="_",c,var)]]$deviance/mod[[paste(sep="_",c,var)]]$null.deviance
    tmpFit<-predict(mod[[paste(sep="_",c,var)]],newdata=datPred %>% dplyr::filter(iso3==c),se.fit=TRUE,type="response")
    datPred[datPred$iso3==c,var]<-tmpFit$fit
    tmpFit<-predict(mod[[paste(sep="_",c,var)]],newdata=datPred %>% dplyr::filter(iso3==c),se.fit=TRUE,type="link")
    datPred[datPred$iso3==c,paste(sep="_",var,"SE")]<-tmpFit$se.fit
  }
}
# Show data
datPred %>%
  dplyr::select(!contains("SE")) %>%
  knitr::kable(col.names=c("Country code","Year","Men (Expected)","Women (expected)","Children (expected)","Adults (expected)","Elderly (expected)")) %>%
  kableExtra::kable_styling(full_width = FALSE)

Country code	Year	Men (Expected)	Women (expected)	Children (expected)	Adults (expected)	Elderly (expected)
AZE	2013	3113.303	1148.4123	1.932870e+02	4072.803	186.29383
BGD	2013	100753.833	64987.3691	6.685641e+03	143777.604	22349.28316
BLR	2013	3259.769	1230.7701	2.011674e+01	4003.023	492.42148
BWA	2013	3628.144	2663.5362	4.945617e+02	5822.708	473.69726
BRA	2013	48965.105	23356.8678	2.215928e+03	65341.344	6966.31057
CMR	2013	14311.678	10829.6561	1.604027e+03	23961.625	1181.20610
CAF	2013	4595.443	2832.9579	1.240456e+03	7133.226	272.78485
CHN	2013	579583.433	254756.1051	4.050537e+03	665724.062	169490.87228
COG	2013	3914.981	3191.4208	8.522540e+02	6633.329	470.79309
PRK	2013	59907.801	42507.7168	6.562807e+03	95428.270	6675.73216
COD	2013	42116.790	31219.2381	9.830887e+03	69453.783	3971.35320
SWZ	2013	3235.184	2594.2919	6.205233e+02	5522.506	305.66258
ETH	2013	68627.101	55338.3588	2.294536e+04	117885.148	6082.75014
GAB	2013	3004.032	2191.3802	5.109991e+02	4737.301	469.49372
GIN	2013	7045.191	3724.9717	1.999814e+02	10304.780	470.67079
GNB	2013	1331.747	801.5251	9.709450e+01	2043.605	89.59776
IND	2013	956871.742	441857.1902	8.291735e+04	1287594.830	109849.99356
IDN	2013	158561.374	109409.6958	2.086127e+04	247210.215	21148.04690
KAZ	2013	10245.019	6241.8308	4.725768e+02	15474.266	1053.90846
KEN	2013	47143.432	29885.1452	7.517334e+03	73354.701	3701.32707
KGZ	2013	4099.522	2948.9480	6.896792e+02	6668.675	464.75961
LSO	2013	4661.220	3412.3302	3.310932e+02	7677.809	479.28516
LBR	2013	2713.628	1419.3507	2.511182e+02	3999.078	132.92224
MWI	2013	8395.859	5924.0033	1.780961e+03	13383.684	944.62041
MNG	2013	2337.017	1783.2874	4.074439e+02	3924.119	197.08620
MMR	2013	63561.636	36067.2283	4.060423e+04	87941.414	11809.31936
NAM	2013	4963.391	3525.6234	1.077943e+03	7943.786	543.81761
NPL	2013	18172.432	9867.8329	2.204560e+03	25414.763	2938.27031
NGA	2013	52337.234	34377.9101	4.728366e+03	80815.327	5895.99626
PAK	2013	144628.380	146902.6859	2.834249e+04	263951.046	27965.36026
PER	2013	15539.038	11478.8116	1.750235e+03	23832.274	3078.88845
PHL	2013	75640.370	37189.9563	2.177264e+04	97998.116	15030.54566
MDA	2013	3098.148	1172.5262	1.261888e+02	4030.302	244.00000
RUS	2013	75672.760	32172.0847	3.651786e+03	101558.243	6358.93114
SLE	2013	4359.948	2545.1341	1.350429e+02	6591.695	316.83755
SOM	2013	5959.074	3528.9422	2.888635e+03	8371.544	1123.18482
ZAF	2013	160568.594	121276.9798	3.654614e+04	269568.848	12290.38989
TJK	2013	3046.356	2556.6359	3.236003e+02	5167.985	438.01914
THA	2013	22697.198	9819.0815	2.507811e+02	43168.956	12701.34691
UKR	2013	23388.922	9613.1648	5.857165e+02	30392.192	2610.66683
TZA	2013	31057.176	19938.9258	4.637898e+03	46581.547	4620.92227
UZB	2013	10382.133	6947.8125	1.866129e+03	15200.720	2132.51523
VNM	2013	80672.225	20941.9939	1.701225e+03	85354.200	15507.77302
ZMB	2013	21797.246	14257.1924	2.807771e+03	34416.145	1603.48166
ZWE	2013	16611.670	13002.4299	2.488640e+03	27764.826	1824.06238
AZE	2014	2941.937	1153.4287	1.903565e+02	3898.695	196.89346
BGD	2014	108679.256	71957.3294	7.415596e+03	155216.616	25638.85024
BLR	2014	2892.259	1089.1887	1.790463e+01	3516.870	463.98191
BWA	2014	3211.462	2333.5292	4.280198e+02	5099.143	445.26066
BRA	2014	50263.652	23473.7887	2.261234e+03	66537.623	7199.55607
CMR	2014	14237.868	10433.4119	1.528377e+03	23435.605	1237.31674
CAF	2014	4751.872	3039.3845	1.316833e+03	7485.856	291.72043
CHN	2014	567371.048	250414.0457	4.367600e+03	645085.700	172687.87337
COG	2014	4221.226	3387.4104	8.705390e+02	7095.736	511.66672
PRK	2014	59506.869	40305.4932	6.179505e+03	93529.702	6088.33392
COD	2014	48544.835	36290.2023	1.117982e+04	80057.780	4834.54846
SWZ	2014	2831.010	2221.1076	4.710685e+02	4787.771	264.56562
ETH	2014	66186.581	53059.4878	2.015328e+04	113310.699	5936.55966
GAB	2014	3044.548	2147.5706	4.729433e+02	4770.805	424.77959
GIN	2014	7354.068	3957.0418	2.724173e+02	10783.515	528.26282
GNB	2014	1332.812	795.8956	1.016208e+02	2038.157	90.52824
IND	2014	995129.840	476795.8867	9.007158e+04	1351614.455	119808.55457
IDN	2014	175039.880	121545.9817	2.579846e+04	271583.353	25060.49082
KAZ	2014	9556.667	5877.2557	4.459199e+02	14330.412	1101.88978
KEN	2014	47806.842	29285.2623	7.691387e+03	73108.730	3994.93959
KGZ	2014	3979.521	2857.0529	5.984863e+02	6367.616	511.61679
LSO	2014	4590.991	3173.9622	3.226708e+02	7249.241	546.93751
LBR	2014	2915.631	1616.4472	3.411620e+02	4368.525	164.37322
MWI	2014	8535.539	5839.6975	1.717852e+03	13355.500	1020.23901
MNG	2014	2301.206	1752.5625	4.058571e+02	3851.905	201.86001
MMR	2014	64819.515	36553.2424	3.721407e+04	88891.617	12527.00142
NAM	2014	4866.742	3375.8957	1.002072e+03	7703.852	539.41255
NPL	2014	18321.030	10018.8667	2.095121e+03	25229.168	3273.90142
NGA	2014	54121.359	34855.3076	5.244496e+03	82736.898	6239.88266
PAK	2014	147031.239	146564.5289	3.119215e+04	264002.159	29738.14530
PER	2014	16147.074	11309.5550	1.674830e+03	24177.240	3225.33054
PHL	2014	93445.125	46426.8604	2.509185e+04	120512.451	19464.22701
MDA	2014	2883.901	1077.1325	1.211351e+02	3716.818	244.00000
RUS	2014	70632.501	29995.0019	3.314516e+03	94437.534	6213.32937
SLE	2014	4986.703	2958.3716	2.247430e+02	7546.965	395.39619
SOM	2014	6270.350	3799.8453	3.029075e+03	8936.518	1136.21064
ZAF	2014	151755.084	112025.3497	3.125662e+04	251646.974	12134.33230
TJK	2014	3038.699	2529.0096	3.332232e+02	5116.918	451.87830
THA	2014	27003.309	11860.1382	3.172773e+02	46337.105	13745.00186
UKR	2014	22281.202	9125.3059	5.850000e+02	28891.950	2514.51717
TZA	2014	32616.265	20799.8635	5.433238e+03	48251.778	5247.65800
UZB	2014	9785.497	6812.5211	1.904016e+03	14442.779	2155.35979
VNM	2014	78990.647	22065.9416	1.699114e+03	84631.015	15985.56392
ZMB	2014	21857.929	13663.0102	2.678740e+03	33876.298	1645.52968
ZWE	2014	16020.779	11929.6357	2.192198e+03	26132.900	1819.64283
AZE	2015	2780.004	1158.4669	1.874704e+02	3732.030	208.09618
BGD	2015	117228.104	79674.8250	8.225250e+03	167565.721	29412.60517
BLR	2015	2566.182	963.8941	1.593577e+01	3089.759	437.18485
BWA	2015	2842.635	2044.4093	3.704308e+02	4465.492	418.53114
BRA	2015	51596.636	23591.2948	2.307465e+03	67755.803	7440.61109
CMR	2015	14164.438	10051.6657	1.456296e+03	22921.133	1296.09281
CAF	2015	4913.625	3260.8526	1.397911e+03	7855.917	311.97044
CHN	2015	555415.991	246145.9923	4.709481e+03	625087.156	175945.17751
COG	2015	4551.427	3595.4361	8.892164e+02	7590.378	556.08895
PRK	2015	59108.621	38217.3616	5.818590e+03	91668.907	5552.62091
COD	2015	55953.956	42184.8469	1.271385e+04	92280.765	5885.36392
SWZ	2015	2477.330	1901.6053	3.576103e+02	4150.789	228.99424
ETH	2015	63832.851	50874.4623	1.770096e+04	108913.758	5793.88267
GAB	2015	3085.610	2104.6367	4.377216e+02	4804.547	384.32399
GIN	2015	7676.488	4203.5701	3.710905e+02	11284.491	592.90191
GNB	2015	1333.878	790.3057	1.063581e+02	2032.723	91.46838
IND	2015	1034917.592	514497.2687	9.784309e+04	1418817.156	130669.91887
IDN	2015	193230.917	135028.4868	3.190413e+04	298359.506	29696.74708
KAZ	2015	8914.564	5533.9747	4.207666e+02	13271.111	1152.05555
KEN	2015	48479.588	28697.4208	7.869469e+03	72863.583	4311.84330
KGZ	2015	3863.033	2768.0215	5.193513e+02	6080.149	563.19813
LSO	2015	4521.821	2952.2453	3.144627e+02	6844.595	624.13916
LBR	2015	3132.671	1840.9132	4.634929e+02	4772.103	203.26589
MWI	2015	8677.543	5756.5914	1.656980e+03	13327.376	1101.91103
MNG	2015	2265.944	1722.3669	4.042765e+02	3781.019	206.74946
MMR	2015	66102.288	37045.8057	3.410696e+04	89852.087	13288.29883
NAM	2015	4771.976	3232.5267	9.315423e+02	7471.166	535.04316
NPL	2015	18470.843	10172.2123	1.991114e+03	25044.928	3647.87081
NGA	2015	55966.303	35339.3346	5.816964e+03	84704.159	6603.82638
PAK	2015	149474.018	146227.1503	3.432832e+04	264053.282	31623.31104
PER	2015	16778.903	11142.7942	1.602673e+03	24527.199	3378.73791
PHL	2015	115440.886	57957.9429	2.891705e+04	148199.286	25205.74713
MDA	2015	2684.470	989.4997	1.162837e+02	3427.717	244.00000
RUS	2015	65927.954	27965.2422	3.008396e+03	87816.090	6071.06148
SLE	2015	5703.556	3438.7038	3.740251e+02	8640.673	493.43313
SOM	2015	6597.885	4091.5446	3.176344e+03	9539.620	1149.38752
ZAF	2015	143425.342	103479.4814	2.673268e+04	234916.608	11980.25626
TJK	2015	3031.060	2501.6819	3.431322e+02	5066.356	466.17597
THA	2015	32126.375	14325.4618	4.014054e+02	49737.764	14874.41272
UKR	2015	21225.945	8662.2054	5.842844e+02	27465.763	2421.90865
TZA	2015	34253.621	21697.9754	6.364970e+03	49981.897	5959.39790
UZB	2015	9223.149	6679.8642	1.942672e+03	13722.630	2178.44907
VNM	2015	77344.121	23250.2111	1.697007e+03	83913.958	16478.07545
ZMB	2015	21918.781	13093.5912	2.555638e+03	33344.919	1688.68033
ZWE	2015	15450.905	10945.3547	1.931068e+03	24596.892	1815.23399
AZE	2016	2626.985	1163.5272	1.846281e+02	3572.489	219.93631
BGD	2016	126449.414	88220.0298	9.123304e+03	180897.327	33741.81506
BLR	2016	2276.868	853.0127	1.418341e+01	2714.518	411.93544
BWA	2016	2516.167	1791.1109	3.205904e+02	3910.582	393.40623
BRA	2016	52964.971	23709.3891	2.354642e+03	68996.285	7689.73708
CMR	2016	14091.387	9683.8872	1.387614e+03	22417.954	1357.66090
CAF	2016	5080.884	3498.4582	1.483982e+03	8244.273	333.62612
CHN	2016	543712.837	241950.6836	5.078124e+03	605708.594	179263.92216
COG	2016	4907.457	3816.2369	9.082945e+02	8119.500	604.36785
PRK	2016	58713.038	36237.4112	5.478754e+03	89845.133	5064.04532
COD	2016	64493.889	49036.9631	1.445837e+04	106369.919	7164.57984
SWZ	2016	2167.835	1628.0628	2.714789e+02	3598.552	198.20550
ETH	2016	61562.824	48779.4176	1.554704e+04	104687.437	5654.63473
GAB	2016	3127.225	2062.5612	4.051230e+02	4838.526	347.72135
GIN	2016	8013.043	4465.4574	5.055044e+02	11808.741	665.45033
GNB	2016	1334.945	784.7550	1.113162e+02	2027.304	92.41829
IND	2016	1076296.156	555179.7885	1.062851e+05	1489361.197	142515.93102
IDN	2016	213312.458	150006.5407	3.945481e+04	327775.594	35190.72286
KAZ	2016	8315.604	5210.7443	3.970322e+02	12290.113	1204.50522
KEN	2016	49161.800	28121.3790	8.051675e+03	72619.259	4653.88581
KGZ	2016	3749.955	2681.7644	4.506800e+02	5805.660	619.97991
LSO	2016	4453.693	2746.0165	3.064634e+02	6462.536	712.23803
LBR	2016	3365.867	2096.5495	6.296883e+02	5212.966	251.36101
MWI	2016	8821.910	5674.6680	1.598265e+03	13299.310	1190.12103
MNG	2016	2231.221	1692.6916	4.027020e+02	3711.439	211.75733
MMR	2016	67410.447	37545.0063	3.125927e+04	90822.935	14095.86220
NAM	2016	4679.055	3095.2463	8.659765e+02	7245.508	530.70917
NPL	2016	18621.881	10327.9049	1.892271e+03	24862.033	4064.55777
NGA	2016	57874.140	35830.0832	6.451921e+03	86718.197	6988.99726
PAK	2016	151957.383	145890.5483	3.777982e+04	264104.415	33627.98155
PER	2016	17435.454	10978.4922	1.533625e+03	24882.223	3539.44184
PHL	2016	142614.162	72353.0111	3.332541e+04	182246.965	32640.88977
MDA	2016	2498.830	908.9966	1.116267e+02	3161.103	244.00000
RUS	2016	61536.758	26072.8361	2.730548e+03	81658.906	5932.05113
SLE	2016	6523.458	3997.0246	6.224654e+02	9892.881	615.77794
SOM	2016	6942.530	4405.6365	3.330772e+03	10183.424	1162.71721
ZAF	2016	135552.814	95585.5357	2.286351e+04	219298.536	11828.13660
TJK	2016	3023.441	2474.6495	3.533358e+02	5016.294	480.92602
THA	2016	38221.389	17303.2432	5.078407e+02	53387.995	16096.62596
UKR	2016	20220.666	8222.6067	5.835697e+02	26109.978	2332.71086
TZA	2016	35973.173	22634.8667	7.456481e+03	51774.051	6767.67109
UZB	2016	8693.117	6549.7904	1.982112e+03	13038.389	2201.78570
VNM	2016	75731.915	24498.0398	1.694901e+03	83202.976	16985.76115
ZMB	2016	21979.803	12547.9032	2.438194e+03	32821.875	1732.96252
ZWE	2016	14901.303	10042.2840	1.701043e+03	23151.166	1810.83583
AZE	2017	2482.388	1168.6096	1.818289e+02	3419.769	232.45010
BGD	2017	136396.085	97681.7162	1.011941e+04	195289.601	38708.23672
BLR	2017	2020.171	754.8866	1.262376e+01	2384.850	388.14430
BWA	2017	2227.192	1569.1957	2.774558e+02	3424.629	369.78959
BRA	2017	54369.593	23828.0746	2.402783e+03	70259.478	7947.20430
CMR	2017	14018.713	9329.5653	1.322171e+03	21925.822	1422.15365
CAF	2017	5253.836	3753.3772	1.575352e+03	8651.827	356.78504
CHN	2017	532256.281	237826.8796	5.475622e+03	586930.794	182645.26622
COG	2017	5291.337	4050.5974	9.277819e+02	8685.508	656.83827
PRK	2017	58320.102	34360.0372	5.158766e+03	88057.643	4618.45955
COD	2017	74337.223	57002.0737	1.644226e+04	122610.163	8721.84032
SWZ	2017	1897.006	1393.8689	2.060924e+02	3119.788	171.55636
ETH	2017	59373.524	46770.6483	1.365522e+04	100625.115	5518.73341
GAB	2017	3169.402	2021.3268	3.749522e+02	4872.746	314.60470
GIN	2017	8364.354	4743.6606	6.886048e+02	12357.347	746.87590
GNB	2017	1336.012	779.2433	1.165054e+02	2021.899	93.37806
IND	2017	1119329.138	599079.1716	1.154556e+05	1563412.710	155435.85525
IDN	2017	235480.976	166646.0374	4.879250e+04	360091.895	41701.09850
KAZ	2017	7756.887	4906.3932	3.746365e+02	11381.630	1259.34277
KEN	2017	49853.613	27556.9000	8.238100e+03	72375.754	5023.06128
KGZ	2017	3640.186	2598.1953	3.910888e+02	5543.563	682.48644
LSO	2017	4386.591	2554.1937	2.986675e+02	6101.803	812.77227
LBR	2017	3616.422	2387.6844	8.554766e+02	5694.556	310.83602
MWI	2017	8968.678	5593.9105	1.541630e+03	13271.304	1285.39242
MNG	2017	2197.031	1663.5275	4.011337e+02	3643.138	216.88650
MMR	2017	68744.494	38050.9338	2.864934e+04	91804.273	14952.50323
NAM	2017	4587.943	2963.7961	8.050254e+02	7026.665	526.41029
NPL	2017	18774.155	10485.9804	1.798334e+03	24680.474	4528.84181
NGA	2017	59847.013	36327.6466	7.156188e+03	88780.123	7396.63339
PAK	2017	154482.006	145554.7212	4.157835e+04	264155.558	35759.73247
PER	2017	18117.696	10816.6130	1.467552e+03	25242.387	3707.78938
PHL	2017	176183.672	90323.3958	3.840581e+04	224116.844	42269.23644
MDA	2017	2326.028	835.0429	1.071561e+02	2915.226	244.00000
RUS	2017	57438.041	24308.4891	2.478362e+03	75933.430	5796.22372
SLE	2017	7461.224	4645.9964	1.035928e+03	11326.559	768.45768
SOM	2017	7305.177	4743.8400	3.492708e+03	10870.676	1176.20149
ZAF	2017	128112.404	88293.7807	1.955435e+04	204718.807	11677.94848
TJK	2017	3015.841	2447.9091	3.638429e+02	4966.727	496.14278
THA	2017	45472.748	20900.0052	6.424979e+02	57306.115	17419.26705
UKR	2017	19262.998	7805.3173	5.828558e+02	24821.117	2246.79818
TZA	2017	37779.048	23612.2117	8.735172e+03	53630.464	7685.57039
UZB	2017	8193.545	6422.2495	2.022354e+03	12388.267	2225.37232
VNM	2017	74153.316	25812.8389	1.692799e+03	82498.019	17509.08853
ZMB	2017	22040.995	12024.9573	2.326146e+03	32307.035	1778.40591
ZWE	2017	14371.251	9213.7232	1.498418e+03	21790.416	1806.44833
AZE	2018	2345.750	1173.7142	1.790721e+02	3273.578	245.67591
BGD	2018	147125.173	108158.1781	1.122427e+04	210826.932	44405.66068
BLR	2018	1792.415	668.0483	1.123560e+01	2095.218	365.72721
BWA	2018	1971.406	1374.7753	2.401248e+02	2999.064	347.59069
BRA	2018	55811.466	23947.3542	2.451909e+03	71545.798	8213.29202
CMR	2018	13946.413	8988.2076	1.259815e+03	21444.493	1489.70999
CAF	2018	5432.676	4026.8711	1.672349e+03	9079.528	381.55157
CHN	2018	521041.126	233773.3617	5.904236e+03	568735.132	186090.39048
COG	2018	5705.246	4299.3503	9.476874e+02	9290.972	713.86410
PRK	2018	57929.796	32579.9256	4.857468e+03	86305.715	4212.08090
COD	2018	85682.889	66260.9631	1.869837e+04	141329.919	10617.57984
SWZ	2018	1660.011	1193.3634	1.564545e+02	2704.720	148.49026
ETH	2018	57262.079	44844.6014	1.199360e+04	96720.429	5386.09830
GAB	2018	3212.148	1980.9168	3.470282e+02	4907.208	284.64205
GIN	2018	8731.067	5039.1962	9.380266e+02	12931.439	838.26482
GNB	2018	1337.081	773.7703	1.219366e+02	2016.508	94.34780
IND	2018	1164082.685	646449.7832	1.254172e+05	1641146.088	169527.04812
IDN	2018	259953.359	185131.2726	6.034013e+04	395594.349	49415.91065
KAZ	2018	7235.710	4619.8187	3.535042e+02	10540.303	1316.67691
KEN	2018	50555.162	27003.7518	8.428841e+03	72133.066	5421.52206
KGZ	2018	3533.631	2517.2304	3.393770e+02	5293.297	751.29489
LSO	2018	4320.500	2375.7708	2.910699e+02	5761.206	927.49720
LBR	2018	3885.629	2719.2474	1.162226e+03	6220.637	384.38352
MWI	2018	9117.889	5514.3023	1.487002e+03	13243.357	1388.29045
MNG	2018	2163.365	1634.8660	3.995715e+02	3576.095	222.13992
MMR	2018	70104.942	38563.6788	2.625733e+04	92796.214	15861.20449
NAM	2018	4498.605	2837.9283	7.483643e+02	6814.432	522.14622
NPL	2018	18927.673	10646.4755	1.709061e+03	24500.241	5046.15984
NGA	2018	61887.139	36832.1196	7.937329e+03	90891.075	7828.04506
PAK	2018	157048.573	145219.6671	4.575880e+04	264206.711	38026.61973
PER	2018	18826.634	10657.1206	1.404326e+03	25607.763	3884.14408
PHL	2018	217655.006	112757.1017	4.426071e+04	275606.015	54737.73422
MDA	2018	2165.175	767.1060	1.028646e+02	2688.475	244.00000
RUS	2018	53612.324	22663.5353	2.249466e+03	70609.393	5663.50639
SLE	2018	8533.796	5400.3377	1.724028e+03	12968.007	958.99375
SOM	2018	7686.768	5108.0060	3.662518e+03	11604.309	1189.84215
ZAF	2018	121080.394	81558.2782	1.672414e+04	191108.389	11529.66738
TJK	2018	3008.260	2421.4577	3.746624e+02	4917.649	511.84100
THA	2018	54099.835	25244.4130	8.128604e+02	61511.784	18850.58802
UKR	2018	18350.686	7409.2049	5.821429e+02	23595.879	2164.04963
TZA	2018	39675.578	24631.7572	1.023314e+04	55553.441	8727.96437
UZB	2018	7722.682	6297.1922	2.063412e+03	11770.560	2249.21161
VNM	2018	72607.622	27198.2028	1.690699e+03	81799.034	18048.53950
ZMB	2018	22102.357	11523.8057	2.219248e+03	31800.272	1825.04097
ZWE	2018	13860.053	8453.5246	1.319930e+03	20509.645	1802.07145
AZE	2019	2216.633	1178.8411	1.763571e+02	3133.635	259.65422
BGD	2019	158698.224	119758.2509	1.244977e+04	227600.419	50941.68237
BLR	2019	1590.336	591.1995	1.000009e+01	1840.761	344.60480
BWA	2019	1744.995	1204.4433	2.078167e+02	2626.382	326.72442
BRA	2019	57291.577	24067.2309	2.502039e+03	72855.669	8488.28887
CMR	2019	13874.487	8659.3398	1.200400e+03	20973.731	1560.47545
CAF	2019	5617.604	4320.2935	1.775317e+03	9528.372	408.03728
CHN	2019	510062.284	229788.9319	6.366400e+03	551103.561	189600.49798
COG	2019	6151.532	4563.3794	9.680199e+02	9938.642	775.84083
PRK	2019	57542.102	30892.0372	4.573766e+03	84588.643	3841.45955
COD	2019	98760.179	77023.7809	2.126405e+04	162907.752	12925.36868
SWZ	2019	1452.625	1021.7002	1.187720e+02	2344.874	128.52544
ETH	2019	55225.723	42997.8704	1.053418e+04	92967.262	5256.65089
GAB	2019	3255.470	1941.3147	3.211839e+02	4941.914	257.53302
GIN	2019	9113.857	5353.1440	1.277792e+03	13532.202	940.83625
GNB	2019	1338.150	768.3358	1.276210e+02	2011.132	95.32761
IND	2019	1210625.589	697566.1014	1.362384e+05	1722744.394	184895.69217
IDN	2019	286969.036	205666.9850	7.462070e+04	434597.088	58557.98320
KAZ	2019	6749.550	4349.9826	3.335639e+02	9761.166	1376.62130
KEN	2019	51266.582	26461.7070	8.623998e+03	71891.191	5851.59126
KGZ	2019	3430.195	2438.7885	2.945028e+02	5054.331	827.04063
LSO	2019	4255.405	2209.8115	2.836657e+02	5439.621	1058.41584
LBR	2019	4174.876	3096.8525	1.578967e+03	6795.320	475.33323
MWI	2019	9269.581	5435.8270	1.434310e+03	13215.469	1499.42566
MNG	2019	2130.215	1606.6982	3.980153e+02	3510.285	227.52058
MMR	2019	71492.313	39083.3331	2.406503e+04	93798.873	16825.12982
NAM	2019	4411.007	2717.4059	6.956913e+02	6608.610	517.91670
NPL	2019	19082.447	10809.4270	1.624219e+03	24321.324	5622.56978
NGA	2019	63996.811	37343.5981	8.803736e+03	93052.221	8284.61900
PAK	2019	159657.781	144885.3842	5.035956e+04	264257.874	40437.20990
PER	2019	19563.313	10499.9800	1.343823e+03	25978.429	4068.88679
PHL	2019	268888.150	140762.6881	5.100817e+04	338924.438	70884.16542
MDA	2019	2015.446	704.6962	9.874498e+01	2479.360	244.00000
RUS	2019	50041.422	21129.8954	2.041711e+03	65658.648	5533.82791
SLE	2019	9760.553	6277.1567	2.869186e+03	14847.333	1196.77251
SOM	2019	8088.291	5500.1277	3.840583e+03	12387.454	1203.64100
ZAF	2019	114434.367	75336.5944	1.430357e+04	178402.839	11383.26909
TJK	2019	3000.699	2395.2922	3.858036e+02	4869.056	528.03593
THA	2019	64363.652	30491.8769	1.028396e+03	66026.105	20399.51897
UKR	2019	17481.581	7033.1949	5.814308e+02	22431.121	2084.34868
TZA	2019	41667.316	25695.3255	1.198800e+04	57545.369	9911.73826
UZB	2019	7278.878	6174.5700	2.105304e+03	11183.654	2273.30628
VNM	2019	71094.147	28657.9185	1.688601e+03	81105.971	18604.61083
ZMB	2019	22163.889	11043.5400	2.117263e+03	31301.457	1872.89893
ZWE	2019	13367.039	7756.0478	1.162702e+03	19304.155	1797.70519
AZE	2020	2094.623	1183.9903	1.736833e+02	2999.676	274.42786
BGD	2020	171181.627	132602.4431	1.380906e+04	245708.413	58439.73409
BLR	2020	1411.039	523.1910	8.900447e+00	1617.207	324.70231
BWA	2020	1544.588	1055.2151	1.798555e+02	2300.011	307.11077
BRA	2020	58810.940	24187.7077	2.553194e+03	74189.520	8772.49314
CMR	2020	13802.931	8342.5049	1.143786e+03	20513.303	1634.60247
CAF	2020	5808.826	4635.0965	1.884625e+03	9999.406	436.36151
CHN	2020	499314.778	225872.4127	6.864740e+03	534018.592	193176.81445
COG	2020	6632.729	4843.6230	9.887887e+02	10631.461	843.19830
PRK	2020	57157.003	29291.5944	4.306635e+03	82905.732	3503.44920
COD	2020	113833.381	89534.8112	2.418177e+04	187780.024	15734.76801
SWZ	2020	1271.147	874.7304	9.016542e+01	2032.903	111.24494
ETH	2020	53261.783	41227.1891	9.252340e+03	89359.733	5130.31456
GAB	2020	3299.376	1902.5043	2.972643e+02	4976.865	233.00582
GIN	2020	9513.430	5686.6511	1.740626e+03	14160.876	1055.95847
GNB	2020	1339.220	762.9394	1.335703e+02	2005.771	96.31759
IND	2020	1259029.393	752724.3081	1.479933e+05	1808399.794	201657.59602
IDN	2020	316792.321	228480.6243	9.228103e+04	477445.216	69391.36305
KAZ	2020	6296.054	4095.9072	3.147484e+02	9039.622	1439.29478
KEN	2020	51988.014	25930.5425	8.823673e+03	71650.128	6315.77625
KGZ	2020	3329.786	2362.7910	2.555622e+02	4826.152	910.42308
LSO	2020	4191.290	2055.4453	2.764497e+02	5135.986	1207.81398
LBR	2020	4485.654	3526.8933	2.145140e+03	7423.093	587.80273
MWI	2020	9423.797	5358.4685	1.383485e+03	13187.640	1619.45744
MNG	2020	2097.573	1579.0158	3.964652e+02	3445.687	233.03157
MMR	2020	72907.140	39609.9899	2.205577e+04	94812.366	17847.63531
NAM	2020	4325.114	2602.0019	6.467256e+02	6409.004	513.72144
NPL	2020	19238.487	10974.8726	1.543589e+03	24143.714	6264.82155
NGA	2020	66178.400	37862.1794	9.764717e+03	95264.753	8767.82279
PAK	2020	162310.339	144551.8709	5.542290e+04	264309.046	43000.61263
PER	2020	20328.817	10345.1564	1.285927e+03	26354.459	4262.41648
PHL	2020	332180.907	175724.0480	5.878428e+04	416789.796	91793.43973
MDA	2020	1876.072	647.3639	9.479034e+01	2286.511	244.00000
RUS	2020	46708.364	19700.0368	1.853144e+03	61055.022	5407.11870
SLE	2020	11163.660	7296.3392	4.774999e+03	16999.012	1493.50758
SOM	2020	8510.788	5922.3510	4.027306e+03	13223.450	1217.59988
ZAF	2020	108153.136	69589.5325	1.223333e+04	166541.998	11238.72969
TJK	2020	2993.156	2369.4094	3.972762e+02	4820.943	544.74326
THA	2020	76574.720	36830.1120	1.301081e+03	70871.731	22075.72379
UKR	2020	16653.639	6676.2670	5.807195e+02	21323.859	2007.58307
TZA	2020	43759.040	26804.8172	1.404378e+04	59608.719	11256.06741
UZB	2020	6860.579	6054.3356	2.148047e+03	10626.012	2297.65906
VNM	2020	69612.220	30195.9765	1.686507e+03	80418.781	19177.81459
ZMB	2020	22225.594	10583.2899	2.019964e+03	30810.466	1922.01188
ZWE	2020	12891.562	7116.1180	1.024204e+03	18169.519	1793.34950

4. Plot models

We get the following predictions for mean number of notified cases

datObsLong<-dat %>%
  dplyr::select(iso3,year,men,women,adults,children,elderly) %>%
  tidyr::pivot_longer(cols=c(men,women,adults,children,elderly),names_to="group",values_to="observed")
datExpLong<-datPred %>%
  dplyr::select(iso3,year,men,women,adults,children,elderly) %>%
  tidyr::pivot_longer(cols=c(men,women,adults,children,elderly),names_to="group",values_to="expected")
datExpSELong<-datPred %>%
  dplyr::select(iso3,year,men_SE,women_SE,adults_SE,children_SE,elderly_SE) %>%
  tidyr::pivot_longer(cols=c(men_SE,women_SE,adults_SE,children_SE,elderly_SE),names_to="group",values_to="SE")
datExpSELong$group<-gsub(pattern="_SE",replacement="",datExpSELong$group)
datFullLong<-datObsLong %>%
  mutate(
    expected=datExpLong$expected[match(paste(sep="_",iso3,year,group),paste(sep="_",datExpLong$iso3,datExpLong$year,datExpLong$group))],
    expectedLow=exp(log(datExpLong$expected[match(paste(sep="_",iso3,year,group),paste(sep="_",datExpLong$iso3,datExpLong$year,datExpLong$group))])-qnorm(0.975)*datExpSELong$SE[match(paste(sep="_",iso3,year,group),paste(sep="_",datExpLong$iso3,datExpLong$year,datExpLong$group))]),
    expectedUpp=exp(log(datExpLong$expected[match(paste(sep="_",iso3,year,group),paste(sep="_",datExpLong$iso3,datExpLong$year,datExpLong$group))])+qnorm(0.975)*datExpSELong$SE[match(paste(sep="_",iso3,year,group),paste(sep="_",datExpLong$iso3,datExpLong$year,datExpLong$group))]),
      annot=paste(sep="",iso3," ",group)
)
for(j in 1:nrow(datFullLong)){datFullLong$annot[j]<-paste(sep="",datFullLong$annot[j]," (pseudo R2 ",format(nsmall=2,round(digits=2,gof[[paste(sep="_",datFullLong$iso3[j],datFullLong$group[j])]])),")")}

  datFullLong %>%
  ggplot(mapping=aes(x=year)) +
  geom_ribbon(mapping=aes(ymin=expectedLow,ymax=expectedUpp),fill="orange",alpha=0.5) +
  geom_line(mapping=aes(y=expected),col="orange",lwd=3) +
  geom_point(mapping=aes(y=observed),col="steelblue",size=10) + 
  facet_wrap(~paste(sep=" ",iso3,group),ncol = 5,scales = "free_y") +
  theme(axis.text.x=element_text(size=25),axis.text.y=element_text(size=25),axis.title.x=element_text(size=25),axis.title.y=element_text(size=25),strip.text = element_text(size=40))

5. Reformat data

We reformat the data for further analysis

datPredLong<-datPred %>%
  dplyr::filter(year==2020) %>%
  dplyr::select(!contains("SE")) %>%
  tidyr::pivot_longer(cols=c(men,women,children,adults,elderly),names_to="group",values_to="expected")
datPredSELong<-datPred %>%
  dplyr::filter(year==2020) %>%
  dplyr::select(c(iso3,year,contains("SE"))) %>%
  tidyr::pivot_longer(cols=c(men_SE,women_SE,children_SE,adults_SE,elderly_SE),names_to="group",values_to="expected")
datLong<-dat %>% 
  dplyr::filter(year==2020) %>%
  dplyr::select(iso3,men,women,children,adults,elderly) %>%
  tidyr::pivot_longer(cols=c(men,women,children,adults,elderly),names_to="group",values_to="observed") %>%
  dplyr::mutate(
    expected=datPredLong$expected[match(paste(sep="_",iso3,group),paste(sep="_",datPredLong$iso3,datPredLong$group))],
    expected_SE=datPredSELong$expected[match(paste(sep="_",iso3,group,"SE"),paste(sep="_",datPredSELong$iso3,datPredSELong$group))]
  )

6. Compute risks

We create a function to compute relative risks and standard errors

analysisFun<-function(refDat,compDat,B=1e4){
  # refDat = data frame with observed, expected and expected SEs for the reference group for each country
  # compDat = same dataframe but including all comparator groups
  # B = number of parametric bootstrap replicates to sample
  # filter out countries with complete data
  allCountries<-intersect(refDat$iso3[!is.na(refDat$observed) & !is.na(refDat$expected) & !is.na(refDat$expected_SE)],compDat$iso3[!is.na(compDat$observed) & !is.na(compDat$expected) & !is.na(compDat$expected_SE)])
  refDat<-refDat %>%
    dplyr::filter(iso3 %in% allCountries) %>%
    dplyr::mutate(
      observedCC=case_when(
        observed>expected~expected-0.5, # "continuity correction" for cases where observed exceeds expected; should be near 0 risk, but not quite
        TRUE~as.numeric(observed)
      ),
      moreObsThanExp=factor(levels=c("no","yes"),case_when(
        observed>expected~"yes",
        TRUE~"no"
      ))) %>%
    dplyr::mutate(risk=(expected-observedCC)/expected)
  compDat<-compDat %>%
    dplyr::filter(iso3 %in% allCountries) %>%
    dplyr::mutate(
      RR=NA,
      RR_low=NA,
      RR_upp=NA,
      RR_logSE=NA,
      observedCC=case_when(
        observed>expected~expected-0.5, # "continuity correction" for cases where observed exceeds expected; should be near 0 risk, but not quite
        TRUE~as.numeric(observed)
      ),
      moreObsThanExp=factor(levels=c("no","yes"),case_when(
        observed>expected~"yes",
        TRUE~"no"
      ))) %>%
    dplyr::mutate(risk=(expected-observedCC)/expected)
  # compute RRs
  for(c in allCountries){
    idx<-which(compDat$iso3==c) # will be a number of groups for that country
    R0<-refDat$risk[refDat$iso3==c] # should be only 1 such value
    if(refDat$moreObsThanExp[refDat$iso3==c]=="no"){
      compDat$RR[idx]<-compDat$risk[idx]/R0
    }else{
      idx<-which(compDat$iso3==c & compDat$moreObsThanExp=="no")
      compDat$RR[idx]<-compDat$risk[idx]/R0
      idx<-which(compDat$iso3==c & compDat$moreObsThanExp!="no")
      compDat$RR[idx]<-NA # cannot compute RRs where both the reference and the comparator have more observed notifications than expected
    }
  }
  # parametric boostrapping using the estimated SEs
  bootRR<-compDat %>% dplyr::select(iso3,group)
  for(b in 1:B){
    refDat_BS<-refDat
    refDat_BS$expected<-exp(rnorm(n=nrow(refDat),mean=log(refDat$expected),sd=refDat$expected_SE))
    refDat_BS<-refDat_BS %>%
      dplyr::mutate(
        missed=case_when(
          expected-observed>0~expected-observed,
          TRUE~0),
        observedCC=case_when(
          observed>expected~expected-0.5, # "continuity correction" for cases where observed exceeds expected; should be near 0 risk, but not quite
          TRUE~as.numeric(observed)
        ),
        moreObsThanExp=case_when(
          observed>expected~"yes",
          TRUE~"no"
        )) %>%
      dplyr::mutate(risk=(expected-observedCC)/expected)
    compDat_BS<-compDat
    compDat_BS$expected<-exp(rnorm(n=nrow(compDat),mean=log(compDat$expected),sd=compDat$expected_SE))
    compDat_BS<-compDat_BS %>%
      dplyr::mutate(
        missed=case_when(
          expected-observed>0~expected-observed,
          TRUE~0),
        observedCC=case_when(
          observed>expected~expected-0.5, # "continuity correction" for cases where observed exceeds expected; should be near 0 risk, but not quite
          TRUE~as.numeric(observed)
        ),
        moreObsThanExp=case_when(
          observed>expected~"yes",
          TRUE~"no"
        )) %>%
      dplyr::mutate(risk=(expected-observedCC)/expected)   
    for(c in allCountries){
      idx<-which(compDat_BS$iso3==c) # will be a number of groups for that country
      R0<-refDat_BS$risk[refDat_BS$iso3==c] # should be only 1 such value
      if(refDat_BS$moreObsThanExp[refDat_BS$iso3==c]=="no"){
        compDat_BS$RR[idx]<-compDat_BS$risk[idx]/R0
      }else{
        idx<-which(compDat_BS$iso3==c & compDat_BS$moreObsThanExp=="no")
        compDat_BS$RR[idx]<-compDat_BS$risk[idx]/R0
        idx<-which(compDat_BS$iso3==c & compDat_BS$moreObsThanExp!="no")
        compDat_BS$RR[idx]<-NA # cannot compute RRs where both the reference and the comparator have more observed notifications than expected
      }
    }
    bootRR[,paste(sep="_","RR",b)]<-compDat_BS$RR
  }
  compDat[,c("RR_low","RR_upp")]<-t(apply(X=bootRR %>% dplyr::select(contains("RR_")),MARGIN=1,FUN=quantile,probs=c(0.025,0.975),na.rm=TRUE))
  logsd<-function(x){
    x<-x[!is.na(x)] # removes NaNs due to 0/0
    # x[x<1e-4]<-1e-4 # avoids -Inf values when logged
    # x[x>1e4]<-1e4 # avoids +Inf values when logged
    res<-sd(log(x),na.rm=TRUE)
    return(res)
  }
  compDat[,"RR_logSE"]<-apply(X=bootRR %>% dplyr::select(contains("RR_")),MARGIN=1,FUN=logsd)
  return(list=list(compDat=compDat,reference=refDat,bootRR=bootRR))
}
plotFun<-function(analysisObj,maxRR=25){
  # analysisObj = output from analysisFun()
   g1<-analysisObj$compDat %>%
    mutate(iso3=paste(sep="",iso3," (",format(nsmall=2,round(digits=1,100*analysisObj$reference$risk[match(iso3,analysisObj$reference$iso3)])),"%)")) %>%
    ggplot() +
    geom_segment(mapping=aes(y=group,yend=group,x=RR_low,xend=RR_upp)) +
    geom_point(mapping=aes(x=RR,y=group)) +
    geom_vline(xintercept=1,lty=2,col="darkgrey") +
    facet_wrap(~iso3,scales="free_x") +
    labs(caption="Estimated risk ratios with 95% confidence intervals obtained from parametric bootstrapping from fitted linear models.\nRisks of being missed in the reference group are shown in brackets in the panel titles.",title=paste(sep="","Reference group: ",unique(analysisObj$reference$group,".")))
  tmp<-analysisObj$compDat %>%
    mutate(iso3=paste(sep="",iso3," (",format(nsmall=2,round(digits=1,100*analysisObj$reference$risk[match(iso3,analysisObj$reference$iso3)])),"%)"))
  g2<-analysisObj$bootRR %>%
    mutate(iso3=paste(sep="",iso3," (",format(nsmall=2,round(digits=1,100*analysisObj$reference$risk[match(iso3,analysisObj$reference$iso3)])),"%)")) %>%
    pivot_longer(cols=contains("RR_"),names_to = "bootNum",values_to="RR") %>%
    dplyr::filter(abs(RR)<maxRR) %>%
    suppressMessages(
    ggplot(mapping=aes(x = RR, y = group, fill = group)) +
    geom_segment(data=tmp,mapping=aes(y=group,yend=group,x=RR_low,xend=RR_upp),col="grey50",size=1,position = position_nudge(y = -0.1)) +
    geom_point(data=tmp,mapping=aes(x=RR,y=group),col="grey50",size=1.5,position = position_nudge(y = -0.1)) +
    geom_density_ridges(alpha=0.75) +
    geom_vline(xintercept=1,lty=2,col="darkgrey") +
    scale_fill_manual(values=viridis(length(unique(analysisObj$bootRR$group)))) +
    theme_ridges() +
    theme(legend.position = "none",axis.title.x=element_text(hjust=0.5)) +
    ylab("") +
    xlab("relative risk") +
    facet_wrap(~iso3,scales="free_x") +
    labs(caption="Estimated risk ratios with 95% confidence intervals obtained from parametric bootstrapping from fitted linear models.\nDensity histograms show the empirical distributions for the risk ratio computed from the parametric boostrapped samples.\nGrey bars below each histogram show the derived 95% confidence intervals for the risk ratio estimates.\nRisks of being missed in the reference group are shown in brackets in the panel titles.",title=paste(sep="","Reference group: ",unique(analysisObj$reference$group,"."))))
  return(list(forestPlot=g1,ridgePlot=g2))
}

7. Missed cases

We show missed cases

refDat<-datLong
refDat<-refDat %>%
  dplyr::mutate(
    missed=case_when(
      expected-observed>0~round(expected-observed),
      TRUE~0),
    missedLow=case_when(
      exp(log(expected)-qnorm(0.975)*expected_SE)-observed>0~round(exp(log(expected)-qnorm(0.975)*expected_SE)-observed),
      TRUE~0),
    missedUpp=case_when(
      exp(log(expected)+qnorm(0.975)*expected_SE)-observed>0~round(exp(log(expected)+qnorm(0.975)*expected_SE)-observed),
      TRUE~0)
  ) %>%
  mutate(
    missed_95CI=paste(sep="",format(big.mark=",",round(missed))," (",format(big.mark=",",missedLow),", ",format(big.mark=",",missedUpp),")")
  ) %>%
  mutate(
    observed=format(big.mark=",",observed,scientific=FALSE),
  )
refDat %>%
  dplyr::select(c(iso3,group,observed,expected,expected_SE,missed_95CI)) %>%
  knitr::kable(col.names=c("Country code","Group","Observed","Expected","SE for log(expected)","Missed (95% CI)"),caption = "Observed and expected counts for 2020 for different groups.\nExpected counts are derived from a Poisson regression model fitted to data from 2013-2019.\nThe 95% CIs for missed cases are derived using a normal approximation of the log(count) scale.") %>%
  kableExtra::kable_styling(full_width = FALSE)

Observed and expected counts for 2020 for different groups. Expected counts are derived from a Poisson regression model fitted to data from 2013-2019. The 95% CIs for missed cases are derived using a normal approximation of the log(count) scale.

Country code	Group	Observed	Expected	SE for log(expected)	Missed (95% CI)
AZE	men	1,610	2.094623e+03	0.0172395	485 ( 415, 557)
AZE	women	857	1.183990e+03	0.0246906	327 ( 271, 386)
AZE	children	91	1.736833e+02	0.0629607	83 ( 63, 105)
AZE	adults	2,282	2.999676e+03	0.0146360	718 ( 633, 805)
AZE	elderly	185	2.744279e+02	0.0544826	89 ( 62, 120)
BGD	men	123,451	1.711816e+05	0.0027237	47,731 ( 46,819, 48,647)
BGD	women	97,264	1.326024e+05	0.0031719	35,338 ( 34,517, 36,165)
BGD	children	9,363	1.380906e+04	0.0098452	4,446 ( 4,182, 4,715)
BGD	adults	181,071	2.457084e+05	0.0022752	64,637 ( 63,544, 65,736)
BGD	elderly	39,644	5.843973e+04	0.0049395	18,796 ( 18,233, 19,364)
BLR	men	1,121	1.411039e+03	0.0194163	290 ( 237, 345)
BLR	women	389	5.231910e+02	0.0317820	134 ( 103, 168)
BLR	children	4	8.900447e+00	0.2454425	5 ( 2, 10)
BLR	adults	1,310	1.617207e+03	0.0179108	307 ( 251, 365)
BLR	elderly	200	3.247023e+02	0.0436315	125 ( 98, 154)
BWA	men	1,258	1.544588e+03	0.0185022	287 ( 232, 344)
BWA	women	811	1.055215e+03	0.0220943	244 ( 199, 291)
BWA	children	160	1.798555e+02	0.0526858	20 ( 2, 39)
BWA	adults	1,845	2.300011e+03	0.0149573	455 ( 389, 523)
BWA	elderly	224	3.071108e+02	0.0447302	83 ( 57, 111)
BRA	men	49,967	5.881094e+04	0.0035957	8,844 ( 8,431, 9,260)
BRA	women	21,522	2.418771e+04	0.0054669	2,666 ( 2,408, 2,926)
BRA	children	1,944	2.553194e+03	0.0171356	609 ( 525, 696)
BRA	adults	64,037	7.418952e+04	0.0031709	10,153 ( 9,693, 10,615)
BRA	elderly	7,452	8.772493e+03	0.0093849	1,320 ( 1,161, 1,483)
CMR	men	13,194	1.380293e+04	0.0103798	609 ( 331, 893)
CMR	women	7,750	8.342505e+03	0.0129984	593 ( 383, 808)
CMR	children	1,158	1.143786e+03	0.0347823	0 ( 0, 66)
CMR	adults	19,432	2.051330e+04	0.0083944	1,081 ( 747, 1,422)
CMR	elderly	1,512	1.634602e+03	0.0314856	123 ( 25, 227)
CAF	men	6,263	5.808826e+03	0.0165235	0 ( 0, 0)
CAF	women	4,643	4.635096e+03	0.0190730	0 ( 0, 169)
CAF	children	1,629	1.884625e+03	0.0296495	256 ( 149, 368)
CAF	adults	10,419	9.999406e+03	0.0127496	0 ( 0, 0)
CAF	elderly	487	4.363615e+02	0.0619959	0 ( 0, 6)
CHN	men	423,296	4.993148e+05	0.0011657	76,019 ( 74,879, 77,161)
CHN	women	194,405	2.258724e+05	0.0017419	31,467 ( 30,698, 32,240)
CHN	children	7,014	6.864740e+03	0.0111519	0 ( 0, 2)
CHN	adults	458,615	5.340186e+05	0.0011134	75,404 ( 74,240, 76,570)
CHN	elderly	159,086	1.931768e+05	0.0019664	34,091 ( 33,348, 34,837)
COG	men	5,902	6.632729e+03	0.0138319	731 ( 553, 913)
COG	women	4,376	4.843623e+03	0.0159477	468 ( 319, 621)
COG	children	897	9.887887e+02	0.0340352	92 ( 28, 160)
COG	adults	9,419	1.063146e+04	0.0108438	1,212 ( 989, 1,441)
COG	elderly	859	8.431983e+02	0.0390854	0 ( 0, 51)
PRK	men	54,238	5.715700e+04	0.0063587	2,919 ( 2,211, 3,636)
PRK	women	30,786	2.929159e+04	0.0085927	0 ( 0, 0)
PRK	children	4,616	4.306635e+03	0.0222982	0 ( 0, 0)
PRK	adults	81,199	8.290573e+04	0.0052296	1,707 ( 861, 2,561)
PRK	elderly	3,825	3.503449e+03	0.0241663	0 ( 0, 0)
COD	men	101,798	1.138334e+05	0.0078378	12,035 ( 10,300, 13,798)
COD	women	76,418	8.953481e+04	0.0089360	13,117 ( 11,562, 14,699)
COD	children	22,340	2.418177e+04	0.0167087	1,842 ( 1,063, 2,647)
COD	adults	165,082	1.877800e+05	0.0061028	22,698 ( 20,465, 24,958)
COD	elderly	13,134	1.573477e+04	0.0226466	2,601 ( 1,918, 3,315)
SWZ	men	1,258	1.271147e+03	0.0201001	13 ( 0, 64)
SWZ	women	783	8.747304e+02	0.0235601	92 ( 52, 133)
SWZ	children	99	9.016542e+01	0.0624948	0 ( 0, 3)
SWZ	adults	1,965	2.032903e+03	0.0157059	68 ( 6, 131)
SWZ	elderly	76	1.112449e+02	0.0670003	35 ( 22, 51)
ETH	men	54,956	5.326178e+04	0.0043896	0 ( 0, 0)
ETH	women	42,398	4.122719e+04	0.0049614	0 ( 0, 0)
ETH	children	10,839	9.252340e+03	0.0096180	0 ( 0, 0)
ETH	adults	92,146	8.935973e+04	0.0033780	0 ( 0, 0)
ETH	elderly	5,208	5.130315e+03	0.0143060	0 ( 0, 68)
GAB	men	2,861	3.299376e+03	0.0172123	438 ( 329, 552)
GAB	women	1,777	1.902504e+03	0.0219699	126 ( 45, 209)
GAB	children	241	2.972643e+02	0.0526682	56 ( 27, 89)
GAB	adults	4,458	4.976865e+03	0.0139323	519 ( 385, 657)
GAB	elderly	180	2.330058e+02	0.0582165	53 ( 28, 81)
GIN	men	9,066	9.513430e+03	0.0093273	447 ( 275, 623)
GIN	women	5,601	5.686651e+03	0.0123305	86 ( 0, 225)
GIN	children	945	1.740626e+03	0.0300024	796 ( 696, 901)
GIN	adults	13,632	1.416088e+04	0.0076689	529 ( 318, 743)
GIN	elderly	1,035	1.055958e+03	0.0306188	21 ( 0, 86)
GNB	men	1,518	1.339220e+03	0.0258302	0 ( 0, 0)
GNB	women	903	7.629394e+02	0.0339156	0 ( 0, 0)
GNB	children	122	1.335703e+02	0.0860798	12 ( 0, 36)
GNB	adults	2,340	2.005771e+03	0.0210225	0 ( 0, 0)
GNB	elderly	81	9.631759e+01	0.0973740	15 ( 0, 36)
IND	men	949,552	1.259029e+06	0.0008653	309,477 (307,344, 311,614)
IND	women	582,210	7.527243e+05	0.0011637	170,514 (168,799, 172,233)
IND	children	97,539	1.479933e+05	0.0026429	50,454 ( 49,690, 51,223)
IND	adults	1,383,074	1.808400e+06	0.0007292	425,326 (422,743, 427,912)
IND	elderly	148,688	2.016576e+05	0.0022737	52,970 ( 52,073, 53,870)
IDN	men	203,415	3.167923e+05	0.0016871	113,377 (112,332, 114,427)
IDN	women	145,149	2.284806e+05	0.0020012	83,332 ( 82,437, 84,230)
IDN	children	35,461	9.228103e+04	0.0035586	56,820 ( 56,179, 57,466)
IDN	adults	313,183	4.774452e+05	0.0013666	164,262 (162,985, 165,543)
IDN	elderly	35,381	6.939136e+04	0.0039103	34,010 ( 33,481, 34,544)
KAZ	men	4,756	6.296054e+03	0.0097862	1,540 ( 1,420, 1,662)
KAZ	women	4,544	4.095907e+03	0.0122743	0 ( 0, 0)
KAZ	children	303	3.147484e+02	0.0443939	12 ( 0, 40)
KAZ	adults	8,155	9.039622e+03	0.0080943	885 ( 742, 1,029)
KAZ	elderly	1,145	1.439295e+03	0.0234890	294 ( 230, 362)
KEN	men	44,034	5.198801e+04	0.0042088	7,954 ( 7,527, 8,385)
KEN	women	22,006	2.593054e+04	0.0057297	3,925 ( 3,635, 4,217)
KEN	children	5,606	8.823673e+03	0.0103206	3,218 ( 3,041, 3,398)
KEN	adults	61,285	7.165013e+04	0.0035146	10,365 ( 9,873, 10,860)
KEN	elderly	4,755	6.315776e+03	0.0129631	1,561 ( 1,402, 1,723)
KGZ	men	2,370	3.329786e+03	0.0176660	960 ( 846, 1,077)
KGZ	women	1,691	2.362791e+03	0.0209325	672 ( 577, 771)
KGZ	children	180	2.555622e+02	0.0571887	76 ( 48, 106)
KGZ	adults	3,480	4.826152e+03	0.0144432	1,346 ( 1,211, 1,485)
KGZ	elderly	581	9.104231e+02	0.0380709	329 ( 264, 400)
LSO	men	2,930	4.191290e+03	0.0141461	1,261 ( 1,147, 1,379)
LSO	women	1,444	2.055445e+03	0.0189770	611 ( 536, 689)
LSO	children	191	2.764497e+02	0.0544529	85 ( 57, 117)
LSO	adults	3,607	5.135986e+03	0.0122044	1,529 ( 1,408, 1,653)
LSO	elderly	767	1.207814e+03	0.0308074	441 ( 370, 516)
LBR	men	3,550	4.485654e+03	0.0147552	936 ( 808, 1,067)
LBR	women	2,349	3.526893e+03	0.0177595	1,178 ( 1,057, 1,303)
LBR	children	1,056	2.145140e+03	0.0279176	1,089 ( 975, 1,210)
LBR	adults	5,569	7.423093e+03	0.0116834	1,854 ( 1,686, 2,026)
LBR	elderly	330	5.878027e+02	0.0477768	258 ( 205, 316)
MWI	men	8,468	9.423797e+03	0.0088796	956 ( 793, 1,121)
MWI	women	5,270	5.358469e+03	0.0113481	88 ( 0, 209)
MWI	children	1,395	1.383485e+03	0.0217523	0 ( 0, 49)
MWI	adults	12,316	1.318764e+04	0.0073410	872 ( 683, 1,063)
MWI	elderly	1,422	1.619457e+03	0.0230044	197 ( 126, 272)
MNG	men	1,908	2.097573e+03	0.0181136	190 ( 116, 265)
MNG	women	1,498	1.579016e+03	0.0208283	81 ( 18, 147)
MNG	children	455	3.964652e+02	0.0422472	0 ( 0, 0)
MNG	adults	3,198	3.445687e+03	0.0140793	248 ( 154, 344)
MNG	elderly	208	2.330316e+02	0.0569696	25 ( 0, 53)
MMR	men	58,384	7.290714e+04	0.0035211	14,523 ( 14,022, 15,028)
MMR	women	31,724	3.960999e+04	0.0047454	7,886 ( 7,519, 8,256)
MMR	children	13,244	2.205577e+04	0.0056995	8,812 ( 8,567, 9,060)
MMR	adults	77,017	9.481237e+04	0.0030585	17,795 ( 17,229, 18,365)
MMR	elderly	13,091	1.784764e+04	0.0074218	4,757 ( 4,499, 5,018)
NAM	men	3,684	4.325114e+03	0.0127932	641 ( 534, 751)
NAM	women	2,181	2.602002e+03	0.0159895	421 ( 341, 504)
NAM	children	644	6.467256e+02	0.0308540	3 ( 0, 43)
NAM	adults	5,435	6.409004e+03	0.0103592	974 ( 845, 1,105)
NAM	elderly	430	5.137214e+02	0.0376852	84 ( 47, 123)
NPL	men	15,950	1.923849e+04	0.0076205	3,288 ( 3,003, 3,578)
NPL	women	9,221	1.097487e+04	0.0101575	1,754 ( 1,538, 1,975)
NPL	children	1,619	1.543589e+03	0.0254228	0 ( 0, 3)
NPL	adults	20,567	2.414371e+04	0.0067028	3,577 ( 3,262, 3,896)
NPL	elderly	4,604	6.264822e+03	0.0146792	1,661 ( 1,483, 1,844)
NGA	men	77,890	6.617840e+04	0.0034193	0 ( 0, 0)
NGA	women	49,417	3.786218e+04	0.0044157	0 ( 0, 0)
NGA	children	8,349	9.764717e+03	0.0096624	1,416 ( 1,233, 1,602)
NGA	adults	116,084	9.526475e+04	0.0028162	0 ( 0, 0)
NGA	elderly	11,223	8.767823e+03	0.0096542	0 ( 0, 0)
PAK	men	123,720	1.623103e+05	0.0023520	38,590 ( 37,844, 39,340)
PAK	women	112,219	1.445519e+05	0.0024425	32,333 ( 31,643, 33,027)
PAK	children	37,051	5.542290e+04	0.0043785	18,372 ( 17,898, 18,850)
PAK	adults	206,818	2.643090e+05	0.0018112	57,491 ( 56,554, 58,431)
PAK	elderly	29,121	4.300061e+04	0.0047940	13,880 ( 13,477, 14,286)
PER	men	14,605	2.032882e+04	0.0076296	5,724 ( 5,422, 6,030)
PER	women	7,952	1.034516e+04	0.0101663	2,393 ( 2,189, 2,601)
PER	children	933	1.285927e+03	0.0280387	353 ( 284, 426)
PER	adults	19,889	2.635446e+04	0.0065497	6,465 ( 6,129, 6,806)
PER	elderly	2,668	4.262416e+03	0.0167902	1,594 ( 1,456, 1,737)
PHL	men	161,117	3.321809e+05	0.0020169	171,064 (169,753, 172,380)
PHL	women	76,964	1.757240e+05	0.0028031	98,760 ( 97,797, 99,728)
PHL	children	18,449	5.878428e+04	0.0044615	40,335 ( 39,823, 40,852)
PHL	adults	199,643	4.167898e+05	0.0017921	217,147 (215,685, 218,613)
PHL	elderly	38,438	9.179344e+04	0.0040271	53,355 ( 52,634, 54,083)
MDA	men	1,309	1.876072e+03	0.0178811	567 ( 502, 634)
MDA	women	402	6.473639e+02	0.0299452	245 ( 208, 284)
MDA	children	56	9.479034e+01	0.0826172	39 ( 25, 55)
MDA	adults	1,567	2.286511e+03	0.0160108	720 ( 649, 792)
MDA	elderly	144	2.440000e+02	0.0541054	100 ( 75, 127)
RUS	men	39,917	4.670836e+04	0.0039962	6,791 ( 6,427, 7,159)
RUS	women	16,774	1.970004e+04	0.0061457	2,926 ( 2,690, 3,165)
RUS	children	1,627	1.853144e+03	0.0194518	226 ( 157, 298)
RUS	adults	52,191	6.105502e+04	0.0034809	8,864 ( 8,449, 9,282)
RUS	elderly	4,500	5.407119e+03	0.0123473	907 ( 778, 1,040)
SLE	men	8,668	1.116366e+04	0.0098937	2,496 ( 2,281, 2,714)
SLE	women	5,489	7.296339e+03	0.0124184	1,807 ( 1,632, 1,987)
SLE	children	1,547	4.774999e+03	0.0228029	3,228 ( 3,019, 3,446)
SLE	adults	13,198	1.699901e+04	0.0080251	3,801 ( 3,536, 4,071)
SLE	elderly	959	1.493508e+03	0.0293400	535 ( 451, 623)
SOM	men	7,802	8.510788e+03	0.0106559	709 ( 533, 888)
SOM	women	5,709	5.922351e+03	0.0130897	213 ( 63, 367)
SOM	children	3,377	4.027306e+03	0.0154339	650 ( 530, 774)
SOM	adults	12,236	1.322345e+04	0.0086802	987 ( 764, 1,214)
SOM	elderly	1,275	1.217600e+03	0.0270121	0 ( 0, 9)
ZAF	men	106,181	1.081531e+05	0.0023996	1,972 ( 1,465, 2,482)
ZAF	women	71,335	6.958953e+04	0.0029081	0 ( 0, 0)
ZAF	children	13,558	1.223333e+04	0.0062916	0 ( 0, 0)
ZAF	adults	173,830	1.665420e+05	0.0019046	0 ( 0, 0)
ZAF	elderly	3,686	1.123873e+04	0.0078505	7,553 ( 7,381, 7,727)
TJK	men	2,068	2.993156e+03	0.0169701	925 ( 827, 1,026)
TJK	women	1,840	2.369409e+03	0.0189024	529 ( 443, 619)
TJK	children	240	3.972762e+02	0.0481979	157 ( 121, 197)
TJK	adults	3,501	4.820943e+03	0.0132650	1,320 ( 1,196, 1,447)
TJK	elderly	407	5.447433e+02	0.0412416	138 ( 95, 184)
THA	men	56,707	7.657472e+04	0.0044645	19,868 ( 19,201, 20,541)
THA	women	27,124	3.683011e+04	0.0065290	9,706 ( 9,238, 10,180)
THA	children	885	1.301081e+03	0.0362683	416 ( 327, 512)
THA	adults	63,197	7.087173e+04	0.0048796	7,675 ( 7,000, 8,356)
THA	elderly	20,634	2.207572e+04	0.0088024	1,442 ( 1,064, 1,826)
UKR	men	12,150	1.665364e+04	0.0061750	4,504 ( 4,303, 4,706)
UKR	women	5,001	6.676267e+03	0.0097102	1,675 ( 1,549, 1,804)
UKR	children	382	5.807195e+02	0.0350199	199 ( 160, 240)
UKR	adults	15,747	2.132386e+04	0.0054430	5,577 ( 5,351, 5,806)
UKR	elderly	1,404	2.007583e+03	0.0180257	604 ( 534, 676)
TZA	men	43,439	4.375904e+04	0.0046897	320 ( 0, 724)
TZA	women	27,762	2.680482e+04	0.0059494	0 ( 0, 0)
TZA	children	13,590	1.404378e+04	0.0092849	454 ( 201, 712)
TZA	adults	59,574	5.960872e+04	0.0039597	35 ( 0, 499)
TZA	elderly	11,627	1.125607e+04	0.0100406	0 ( 0, 0)
UZB	men	5,696	6.860579e+03	0.0094957	1,165 ( 1,038, 1,293)
UZB	women	4,682	6.054336e+03	0.0106075	1,372 ( 1,248, 1,500)
UZB	children	1,733	2.148047e+03	0.0186787	415 ( 338, 495)
UZB	adults	8,591	1.062601e+04	0.0077056	2,035 ( 1,876, 2,197)
UZB	elderly	1,787	2.297659e+03	0.0178581	511 ( 432, 593)
VNM	men	70,754	6.961222e+04	0.0045611	0 ( 0, 0)
VNM	women	27,651	3.019598e+04	0.0073614	2,545 ( 2,112, 2,984)
VNM	children	1,396	1.686507e+03	0.0297922	291 ( 195, 392)
VNM	adults	79,404	8.041878e+04	0.0042883	1,015 ( 342, 1,694)
VNM	elderly	19,001	1.917781e+04	0.0090702	177 ( 0, 521)
ZMB	men	24,602	2.222559e+04	0.0056880	0 ( 0, 0)
ZMB	women	12,674	1.058329e+04	0.0078027	0 ( 0, 0)
ZMB	children	2,724	2.019964e+03	0.0177626	0 ( 0, 0)
ZMB	adults	34,026	3.081047e+04	0.0047241	0 ( 0, 0)
ZMB	elderly	3,250	1.922012e+03	0.0198828	0 ( 0, 0)
ZWE	men	9,622	1.289156e+04	0.0071247	3,270 ( 3,091, 3,451)
ZWE	women	5,194	7.116118e+03	0.0090178	1,922 ( 1,797, 2,049)
ZWE	children	912	1.024204e+03	0.0225822	112 ( 68, 159)
ZWE	adults	13,690	1.816952e+04	0.0058249	4,480 ( 4,273, 4,688)
ZWE	elderly	1,126	1.793349e+03	0.0198993	667 ( 599, 739)

# Total expected cases
#sum(refDat[refDat$group=="men",6])
#sum(refDat[refDat$group=="men",7])
#sum(refDat[refDat$group=="men",8])
#sum(refDat[refDat$group=="men",6])/sum(refDat[refDat$group=="men",4])
#sum(refDat[refDat$group=="women",6])
#sum(refDat[refDat$group=="women",7])
#sum(refDat[refDat$group=="women",8])
#sum(refDat[refDat$group=="women",6])/sum(refDat[refDat$group=="women",4])
#sum(refDat[refDat$group=="children",6])
#sum(refDat[refDat$group=="children",7])
#sum(refDat[refDat$group=="children",8])
#sum(refDat[refDat$group=="children",6])/sum(refDat[refDat$group=="children",4])
#sum(refDat[refDat$group=="adults",6])
#sum(refDat[refDat$group=="adults",7])
#sum(refDat[refDat$group=="adults",8])
#sum(refDat[refDat$group=="adults",6])/sum(refDat[refDat$group=="adults",4])
#sum(refDat[refDat$group=="elderly",6])
#sum(refDat[refDat$group=="elderly",7])
#sum(refDat[refDat$group=="elderly",8])
#sum(refDat[refDat$group=="elderly",6])/sum(refDat[refDat$group=="elderly",4])

8. Sex risk ratios

We calculate the risk ratios for women compared to men and conduct a meta analysis on these

resm<-analysisFun(refDat=datLong %>% filter(group=="men"),compDat=datLong %>% filter(group!="men"))
# Show data
resm$compDat %>%
  dplyr::select(c(iso3,group,contains("RR"))) %>%
  knitr::kable(col.names=c("Country code","Group","RR","RR (95% CI lower bound)","RR (95% CI upper bound)","SE of log(RR)"),caption = "Relative risks of being missed using men as reference. 10,000 parametric bootstrap samples for the CI and SE.") %>%
  kableExtra::kable_styling(full_width = FALSE)

Relative risks of being missed using men as reference. 10,000 parametric bootstrap samples for the CI and SE.

Country code	Group	RR	RR (95% CI lower bound)	RR (95% CI upper bound)	SE of log(RR)
AZE	women	1.193682e+00	1.001955e+00	1.408608e+00	0.0870072
AZE	children	2.057603e+00	1.705694e+00	2.443084e+00	0.0913656
AZE	adults	1.034084e+00	8.975691e-01	1.200304e+00	0.0739524
AZE	elderly	1.408466e+00	1.060039e+00	1.769766e+00	0.1304519
BGD	women	9.557755e-01	9.352435e-01	9.766346e-01	0.0110833
BGD	children	1.154706e+00	1.104512e+00	1.205207e+00	0.0220094
BGD	adults	9.434610e-01	9.262861e-01	9.612703e-01	0.0094257
BGD	elderly	1.153483e+00	1.126038e+00	1.182149e+00	0.0125272
BLR	women	1.247801e+00	9.748168e-01	1.561338e+00	0.1191922
BLR	children	2.678588e+00	1.306514e+00	3.682142e+00	0.2809714
BLR	adults	9.241615e-01	7.500054e-01	1.139925e+00	0.1070713
BLR	elderly	1.868406e+00	1.521454e+00	2.278709e+00	0.1035250
BWA	women	1.247345e+00	9.991516e-01	1.557783e+00	0.1110844
BWA	children	5.949934e-01	6.772710e-02	1.094420e+00	0.7088888
BWA	adults	1.066221e+00	8.755808e-01	1.307831e+00	0.1019580
BWA	elderly	1.458536e+00	1.054672e+00	1.909480e+00	0.1497480
BRA	women	7.328754e-01	6.628154e-01	8.025268e-01	0.0488382
BRA	children	1.586660e+00	1.405642e+00	1.765083e+00	0.0584694
BRA	adults	9.100046e-01	8.606409e-01	9.631436e-01	0.0286872
BRA	elderly	1.000980e+00	8.900398e-01	1.113609e+00	0.0577789
CMR	women	1.609899e+00	9.262728e-01	3.048622e+00	0.3108608
CMR	children	9.909000e-03	7.187900e-03	1.299841e+00	1.8398693
CMR	adults	1.194854e+00	7.165704e-01	2.218753e+00	0.2942349
CMR	elderly	1.700161e+00	3.352149e-01	3.785752e+00	0.7399166
CAF	women		7.488586e+00	4.955032e+02	1.0942112
CAF	children	1.575784e+03	9.821770e+02	2.130498e+03	0.2000206
CAF	adults		1.867809e+00	8.895899e+01	3.6149436
CAF	elderly		1.214052e+01	9.099386e+02	1.1323784
CHN	women	9.150638e-01	8.928570e-01	9.377251e-01	0.0125140
CHN	children	4.784000e-04	4.680000e-04	2.195400e-03	0.6200523
CHN	adults	9.274472e-01	9.106181e-01	9.449529e-01	0.0094298
CHN	elderly	1.159140e+00	1.133681e+00	1.184667e+00	0.0112423
COG	women	8.763173e-01	5.903512e-01	1.257231e+00	0.1917196
COG	children	8.426006e-01	2.731990e-01	1.462911e+00	0.4958565
COG	adults	1.035168e+00	7.918574e-01	1.377976e+00	0.1403882
COG	elderly	5.382400e-03	4.515200e-03	5.261971e-01	1.6440386
PRK	women	3.342000e-04	2.706000e-04	4.344000e-04	0.1204795
PRK	children	2.273400e-03	1.838900e-03	2.976500e-03	0.1671605
PRK	adults	4.031026e-01	1.978464e-01	6.502549e-01	0.3056966
PRK	elderly	2.794500e-03	2.257200e-03	3.665900e-03	0.1504137
COD	women	1.385626e+00	1.181318e+00	1.645328e+00	0.0854904
COD	children	7.203739e-01	4.250734e-01	1.023613e+00	0.2256163
COD	adults	1.143269e+00	9.837396e-01	1.349116e+00	0.0808495
COD	elderly	1.563331e+00	1.177949e+00	1.996138e+00	0.1351917
SWZ	women	1.013923e+01	1.888278e+00	3.415949e+02	1.8767335
SWZ	children	5.361616e-01	1.029050e-01	3.284567e+01	1.3513578
SWZ	adults	3.229532e+00	2.578537e-01	1.383537e+02	2.0375766
SWZ	elderly	3.063248e+01	6.025797e+00	9.543311e+02	1.8737540
ETH	women
ETH	children
ETH	adults
ETH	elderly		2.603498e+01	2.413710e+03	1.1905232
GAB	women	4.964982e-01	1.804933e-01	8.486829e-01	0.4261691
GAB	children	1.424540e+00	7.492687e-01	2.185137e+00	0.2795860
GAB	adults	7.846630e-01	5.596834e-01	1.084601e+00	0.1684168
GAB	elderly	1.712149e+00	9.773615e-01	2.571470e+00	0.2455091
GIN	women	3.202494e-01	1.639800e-03	9.422380e-01	1.7976663
GIN	children	9.718855e+00	7.059799e+00	1.578999e+01	0.2059289
GIN	adults	7.940998e-01	4.429750e-01	1.406916e+00	0.2939224
GIN	elderly	4.220118e-01	8.296200e-03	1.807958e+00	1.8969709
GNB	women
GNB	children	2.320155e+02	2.107883e+01	6.297608e+02	0.8881165
GNB	adults
GNB	elderly	4.259581e+02	6.309046e+01	8.179293e+02	0.6956712
IND	women	9.215774e-01	9.129602e-01	9.303505e-01	0.0047707
IND	children	1.386956e+00	1.371362e+00	1.402409e+00	0.0057322
IND	adults	9.568287e-01	9.501945e-01	9.635319e-01	0.0035651
IND	elderly	1.068609e+00	1.054348e+00	1.082811e+00	0.0068504
IDN	women	1.019081e+00	1.010104e+00	1.028330e+00	0.0046113
IDN	children	1.720432e+00	1.707679e+00	1.733257e+00	0.0037611
IDN	adults	9.613081e-01	9.537588e-01	9.687264e-01	0.0039846
IDN	elderly	1.369476e+00	1.356110e+00	1.382707e+00	0.0050123
KAZ	women	4.991000e-04	4.691000e-04	5.332000e-04	0.0326378
KAZ	children	1.525970e-01	6.577900e-03	4.867228e-01	1.5165506
KAZ	adults	4.000737e-01	3.381065e-01	4.653684e-01	0.0810427
KAZ	elderly	8.359210e-01	6.789684e-01	9.934088e-01	0.0977814
KEN	women	9.892232e-01	9.156356e-01	1.066625e+00	0.0393251
KEN	children	2.383469e+00	2.250461e+00	2.525131e+00	0.0294159
KEN	adults	9.455283e-01	8.893136e-01	1.004247e+00	0.0311357
KEN	elderly	1.615217e+00	1.471113e+00	1.759753e+00	0.0459229
KGZ	women	9.863944e-01	8.602553e-01	1.131344e+00	0.0693882
KGZ	children	1.025769e+00	7.273809e-01	1.312800e+00	0.1508088
KGZ	adults	9.676869e-01	8.630849e-01	1.086273e+00	0.0580410
KGZ	elderly	1.255314e+00	1.055931e+00	1.459626e+00	0.0823629
LSO	women	9.885176e-01	8.853580e-01	1.099347e+00	0.0558217
LSO	children	1.027134e+00	7.626470e-01	1.271844e+00	0.1296853
LSO	adults	9.892644e-01	9.090073e-01	1.078137e+00	0.0441308
LSO	elderly	1.212797e+00	1.065789e+00	1.364899e+00	0.0636423
LBR	women	1.601121e+00	1.408553e+00	1.831896e+00	0.0670985
LBR	children	2.434102e+00	2.161623e+00	2.769356e+00	0.0630118
LBR	adults	1.197450e+00	1.055306e+00	1.370933e+00	0.0665573
LBR	elderly	2.102648e+00	1.782815e+00	2.475901e+00	0.0832979
MWI	women	1.627827e-01	9.061000e-04	3.802753e-01	1.5231965
MWI	children	3.563300e-03	3.135900e-03	3.220863e-01	1.6671740
MWI	adults	6.516734e-01	4.959253e-01	8.351899e-01	0.1323920
MWI	elderly	1.202165e+00	7.779107e-01	1.658640e+00	0.1893889
MNG	women	5.677070e-01	1.210853e-01	1.160483e+00	0.6511140
MNG	children	1.395420e-02	1.020330e-02	2.233450e-02	0.2001713
MNG	adults	7.953683e-01	4.590566e-01	1.381772e+00	0.2726535
MNG	elderly	1.188542e+00	4.333010e-02	2.554623e+00	0.8523718
MMR	women	9.994499e-01	9.538812e-01	1.046781e+00	0.0240347
MMR	children	2.005629e+00	1.941352e+00	2.073615e+00	0.0166614
MMR	adults	9.422182e-01	9.067895e-01	9.791686e-01	0.0196746
MMR	elderly	1.337916e+00	1.274480e+00	1.403827e+00	0.0246966
NAM	women	1.091537e+00	8.769346e-01	1.348825e+00	0.1104978
NAM	children	2.843150e-02	4.731600e-03	4.349893e-01	1.7355207
NAM	adults	1.025256e+00	8.562132e-01	1.233319e+00	0.0930277
NAM	elderly	1.099439e+00	6.680033e-01	1.558078e+00	0.2172009
NPL	women	9.349177e-01	8.211641e-01	1.055746e+00	0.0651265
NPL	children	1.895000e-03	1.746500e-03	1.651390e-02	0.5712275
NPL	adults	8.666723e-01	7.806919e-01	9.634449e-01	0.0535397
NPL	elderly	1.550919e+00	1.392259e+00	1.726821e+00	0.0551275
NGA	women
NGA	children	1.918948e+04	1.697794e+04	2.130096e+04	0.0578601
NGA	adults
NGA	elderly
PAK	women	9.407801e-01	9.203454e-01	9.620620e-01	0.0113008
PAK	children	1.394224e+00	1.362992e+00	1.426032e+00	0.0115418
PAK	adults	9.148624e-01	8.967227e-01	9.324683e-01	0.0098928
PAK	elderly	1.357595e+00	1.324693e+00	1.391518e+00	0.0125036
PER	women	8.215999e-01	7.585097e-01	8.856285e-01	0.0393470
PER	children	9.747544e-01	8.234499e-01	1.119762e+00	0.0781909
PER	adults	8.713079e-01	8.237959e-01	9.210173e-01	0.0285529
PER	elderly	1.328533e+00	1.237909e+00	1.418465e+00	0.0345280
PHL	women	1.091356e+00	1.085049e+00	1.097388e+00	0.0028763
PHL	children	1.332417e+00	1.325085e+00	1.339535e+00	0.0027841
PHL	adults	1.011702e+00	1.006729e+00	1.016641e+00	0.0024928
PHL	elderly	1.128713e+00	1.121126e+00	1.136260e+00	0.0034491
MDA	women	1.253931e+00	1.101588e+00	1.417488e+00	0.0641176
MDA	children	1.353851e+00	1.002455e+00	1.681099e+00	0.1326358
MDA	adults	1.041060e+00	9.365804e-01	1.158527e+00	0.0538946
MDA	elderly	1.355881e+00	1.125435e+00	1.591946e+00	0.0882860
RUS	women	1.021528e+00	9.395748e-01	1.106693e+00	0.0417572
RUS	children	8.392932e-01	6.027518e-01	1.071001e+00	0.1487473
RUS	adults	9.984977e-01	9.403414e-01	1.061270e+00	0.0310953
RUS	elderly	1.153814e+00	1.006202e+00	1.303564e+00	0.0669990
SLE	women	1.108041e+00	9.994532e-01	1.223483e+00	0.0515152
SLE	children	3.023996e+00	2.824332e+00	3.259361e+00	0.0364038
SLE	adults	1.000223e+00	9.176158e-01	1.092928e+00	0.0445189
SLE	elderly	1.600912e+00	1.405905e+00	1.806659e+00	0.0641489
SOM	women	4.325679e-01	1.347415e-01	7.731734e-01	0.5281230
SOM	children	1.938906e+00	1.483613e+00	2.606386e+00	0.1435784
SOM	adults	8.966523e-01	6.534684e-01	1.233676e+00	0.1625686
SOM	elderly	4.930800e-03	4.012900e-03	7.749990e-02	0.6317912
ZAF	women	3.940000e-04	3.133000e-04	5.311000e-04	0.1337049
ZAF	children	2.241400e-03	1.782100e-03	3.021500e-03	0.1338670
ZAF	adults	1.646000e-04	1.309000e-04	2.220000e-04	0.1336548
ZAF	elderly	3.685438e+01	2.930516e+01	4.963667e+01	0.1336770
TJK	women	7.228796e-01	6.175481e-01	8.317301e-01	0.0762151
TJK	children	1.280811e+00	1.067804e+00	1.491767e+00	0.0853703
TJK	adults	8.858043e-01	7.987998e-01	9.807546e-01	0.0522577
TJK	elderly	8.180747e-01	6.086520e-01	1.025387e+00	0.1320042
THA	women	1.015733e+00	9.725716e-01	1.060790e+00	0.0221437
THA	children	1.232569e+00	1.034194e+00	1.413724e+00	0.0804505
THA	adults	4.173760e-01	3.826797e-01	4.517589e-01	0.0425852
THA	elderly	2.517123e-01	1.895550e-01	3.146770e-01	0.1298036
UKR	women	9.278888e-01	8.676755e-01	9.883492e-01	0.0330985
UKR	children	1.265376e+00	1.088402e+00	1.430384e+00	0.0693940
UKR	adults	9.670957e-01	9.254166e-01	1.010856e+00	0.0225587
UKR	elderly	1.111755e+00	1.013587e+00	1.210329e+00	0.0452636
TZA	women	2.550500e-03	1.132900e-03	2.556650e-02	0.8083680
TZA	children	4.418042e+00	1.433025e+00	2.881162e+03	1.6867019
TZA	adults	7.963820e-02	5.642000e-04	1.156770e+02	3.3592309
TZA	elderly	6.073600e-03	2.691700e-03	6.210980e-02	0.8167581
UZB	women	1.335321e+00	1.194121e+00	1.505541e+00	0.0591016
UZB	children	1.138270e+00	9.439278e-01	1.354221e+00	0.0922904
UZB	adults	1.128207e+00	1.012837e+00	1.269313e+00	0.0569501
UZB	elderly	1.309295e+00	1.122108e+00	1.521507e+00	0.0778157
VNM	women	1.173411e+04	9.874507e+03	1.356217e+04	0.0801398
VNM	children	2.398189e+04	1.717093e+04	3.041928e+04	0.1473955
VNM	adults	1.756832e+03	6.135858e+02	2.897260e+03	0.4115728
VNM	elderly	1.283614e+03	1.172528e+02	3.816411e+03	0.9069722
ZMB	women
ZMB	children
ZMB	adults
ZMB	elderly
ZWE	women	1.065008e+00	1.000053e+00	1.134312e+00	0.0322325
ZWE	children	4.319531e-01	2.740941e-01	5.901081e-01	0.1950339
ZWE	adults	9.720844e-01	9.218511e-01	1.026355e+00	0.0274470
ZWE	elderly	1.467251e+00	1.355819e+00	1.584467e+00	0.0397051

gm<-plotFun(resm)
# print(gm$ridgePlot)
# Prep data for meta-analysis
resm1<-as.data.frame(resm$compDat)
resm1<-resm1[resm1$group=="women",]
resm1$Region<-dat[year=="2020",g_whoregion]
resm1$Country<-dat[year=="2020",country]
resm1$observed_M<-as.data.frame(resm$reference[,3])
resm1$expected_M<-as.data.frame(resm$reference[,4])
# Remove RR of Na
resm1<-subset(resm1,!is.na(RR))
# Conduct meta-analysis
Risk_sex<-metagen(studlab=Country,sm="RR",TE=log(RR),subgroup=Region,fixed=FALSE,data=resm1,backtransf=TRUE,lower=log(RR_low),upper=log(RR_upp))
# Countries with evidence of effect
MF_evidence_hi_strong<-sum(exp(Risk_sex$TE)>1.1&(Risk_sex$pval<0.01))
MF_evidence_hi_med<-sum(exp(Risk_sex$TE)>1.1&Risk_sex$pval>0.01&Risk_sex$pval<0.05)
MF_evidence_hi_weak<-sum(exp(Risk_sex$TE)>1.25&Risk_sex$pval>0.05&Risk_sex$pval<0.1)
MF_evidence_lo_strong<-sum(exp(Risk_sex$TE)<1/1.1&(Risk_sex$pval<0.01))
MF_evidence_lo_med<-sum(exp(Risk_sex$TE)<1/1.1&Risk_sex$pval>0.01&Risk_sex$pval<0.05)
MF_evidence_lo_weak<-sum(exp(Risk_sex$TE)<1/1.25&Risk_sex$pval>0.05&Risk_sex$pval<0.1)
# P-value for regional subgroups
# Risk_sex$pval.random.w
print("Meta-analysis for women/men")

## [1] "Meta-analysis for women/men"

Risk_sex

## Number of studies combined: k = 40
## 
##                          RR           95%-CI     z p-value
## Random effects model 0.5908 [0.2456; 1.4210] -1.18  0.2399
## 
## Quantifying heterogeneity:
##  tau^2 = 7.8480 [5.1877; 12.8961]; tau = 2.8014 [2.2777; 3.5911]
##  I^2 = 99.9% [99.9%; 100.0%]; H = 44.44 [43.58; 45.31]
## 
## Test of heterogeneity:
##         Q d.f. p-value
##  77013.97   39       0
## 
## Results for subgroups (random effects model):
##                                         k     RR              95%-CI   tau^2
## Region = European Region                9 0.4551 [0.0852;    2.4306]  6.5715
## Region = South-East Asia Region         7 0.3120 [0.0335;    2.9052]  9.0694
## Region = African Region                16 0.4319 [0.1229;    1.5175]  6.1889
## Region = Region of the Americas         2 0.7788 [0.6964;    0.8708]  0.0046
## Region = Western Pacific Region         4 9.1167 [0.0829; 1002.9025] 22.9231
## Region = Eastern Mediterranean Region   2 0.7249 [0.3532;    1.4878]  0.2024
##                                          tau        Q    I^2
## Region = European Region              2.5635 40112.18 100.0%
## Region = South-East Asia Region       3.0115  4596.85  99.9%
## Region = African Region               2.4877  3524.96  99.6%
## Region = Region of the Americas       0.0675     3.31  69.8%
## Region = Western Pacific Region       4.7878 13348.27 100.0%
## Region = Eastern Mediterranean Region 0.4499     3.04  67.1%
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   2.95    5  0.7079
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau

# Sensitivity analysis removing countries with more cases than expected
resm1s<-resm1[resm1$moreObsThanExp=="no"&(resm1$expected_M>resm1$observed_M),]
Risk_sexS<-metagen(studlab=Country,sm="RR",TE=log(RR),subgroup=Region,fixed=FALSE,data=resm1s,backtransf=TRUE,lower=log(RR_low),upper=log(RR_upp))
print("Sensitivity analysis for women/men removing countries with more cases than expected")

## [1] "Sensitivity analysis for women/men removing countries with more cases than expected"

Risk_sexS

## Number of studies combined: k = 35
## 
##                          RR           95%-CI    z p-value
## Random effects model 1.0214 [0.9574; 1.0897] 0.64  0.5210
## 
## Quantifying heterogeneity:
##  tau^2 = 0.0275 [0.0183; 0.0966]; tau = 0.1659 [0.1354; 0.3108]
##  I^2 = 97.5% [97.1%; 97.9%]; H = 6.35 [5.85; 6.89]
## 
## Test of heterogeneity:
##        Q d.f.  p-value
##  1370.64   34 < 0.0001
## 
## Results for subgroups (random effects model):
##                                         k     RR           95%-CI  tau^2    tau
## Region = European Region                8 1.0627 [0.9234; 1.2231] 0.0361 0.1899
## Region = South-East Asia Region         6 0.9766 [0.9404; 1.0142] 0.0018 0.0420
## Region = African Region                14 1.1346 [1.0073; 1.2779] 0.0281 0.1676
## Region = Region of the Americas         2 0.7788 [0.6964; 0.8708] 0.0046 0.0675
## Region = Western Pacific Region         3 0.9875 [0.8329; 1.1709] 0.0154 0.1239
## Region = Eastern Mediterranean Region   2 0.7249 [0.3532; 1.4878] 0.2024 0.4499
##                                            Q   I^2
## Region = European Region               66.01 89.4%
## Region = South-East Asia Region       237.97 97.9%
## Region = African Region                64.11 79.7%
## Region = Region of the Americas         3.31 69.8%
## Region = Western Pacific Region       189.63 98.9%
## Region = Eastern Mediterranean Region   3.04 67.1%
## 
## Test for subgroup differences (random effects model):
##                      Q d.f. p-value
## Between groups   23.88    5  0.0002
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau

# Sensitivity analysis removing countries with poor fit or limited data
resm1p<-resm1[!resm1$iso3%in%c("AZE","BRA","CMR","CAF","COD","LBR","SLE","PRK","GAB","GNB","KEN","LSO","MWI","MNG","MMR","NGA","PAK","PER","TJK","UZB","VNM","ZMB"),]
Risk_sexP<-metagen(studlab=Country,sm="RR",TE=log(RR),subgroup=Region,fixed=FALSE,data=resm1p,backtransf=TRUE,lower=log(RR_low),upper=log(RR_upp))
print("Sensitivity analysis for women/men removing countries with a poor fit or limited data")

## [1] "Sensitivity analysis for women/men removing countries with a poor fit or limited data"

Risk_sexP

## Number of studies combined: k = 22
## 
##                          RR           95%-CI     z p-value
## Random effects model 0.3884 [0.1307; 1.1545] -1.70  0.0889
## 
## Quantifying heterogeneity:
##  tau^2 = 6.6012 [3.8327; 13.6897]; tau = 2.5693 [1.9577; 3.7000]
##  I^2 = 100.0%; H = 53.01 [51.80; 54.24]
## 
## Test of heterogeneity:
##         Q d.f. p-value
##  59004.61   21       0
## 
## Results for subgroups (random effects model):
##                                         k     RR           95%-CI   tau^2
## Region = South-East Asia Region         5 0.9722 [0.9299; 1.0164]  0.0021
## Region = European Region                6 0.2999 [0.0243; 3.6944]  9.8438
## Region = African Region                 8 0.2029 [0.0177; 2.3259] 11.8174
## Region = Western Pacific Region         2 0.9998 [0.8412; 1.1882]  0.0154
## Region = Eastern Mediterranean Region   1 0.4326 [0.1806; 1.0362]      --
##                                          tau        Q    I^2
## Region = South-East Asia Region       0.0458   236.54  98.3%
## Region = European Region              3.1375 36962.60 100.0%
## Region = African Region               3.4376  3354.44  99.8%
## Region = Western Pacific Region       0.1242   188.38  99.5%
## Region = Eastern Mediterranean Region     --     0.00     --
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   5.83    4  0.2124
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau

9. Age risk ratios

We calculate the risk ratios for children compared to adults and elderly compared to adults, and conduct a meta analysis on these

resa<-analysisFun(refDat=datLong %>% filter(group=="adults"),compDat=datLong %>% filter(group!="adults"))
# Show data
resa$compDat %>%
  dplyr::select(c(iso3,group,contains("RR"))) %>%
  knitr::kable(col.names=c("Country code","Group","RR","RR (95% CI lower bound)","RR (95% CI upper bound)","SE of log(RR)"),caption = "Relative risks of being missed using adults as reference. 10,000 parametric bootstrap samples for the CI and SE.") %>%
  kableExtra::kable_styling(full_width = FALSE)

Relative risks of being missed using adults as reference. 10,000 parametric bootstrap samples for the CI and SE.

Country code	Group	RR	RR (95% CI lower bound)	RR (95% CI upper bound)	SE of log(RR)
AZE	men	9.670396e-01	8.335580e-01	1.120931e+00	0.0750656
AZE	women	1.154338e+00	9.807447e-01	1.343334e+00	0.0801841
AZE	children	1.989783e+00	1.657591e+00	2.325858e+00	0.0853989
AZE	elderly	1.362043e+00	1.025529e+00	1.681152e+00	0.1246770
BGD	men	1.059927e+00	1.040510e+00	1.079828e+00	0.0094761
BGD	women	1.013052e+00	9.917791e-01	1.034772e+00	0.0107885
BGD	children	1.223904e+00	1.171877e+00	1.275803e+00	0.0215547
BGD	elderly	1.222608e+00	1.193556e+00	1.251993e+00	0.0120883
BLR	men	1.082062e+00	8.779121e-01	1.341827e+00	0.1073190
BLR	women	1.350198e+00	1.044975e+00	1.698142e+00	0.1229167
BLR	children	2.898398e+00	1.462525e+00	3.979088e+00	0.2836551
BLR	elderly	2.021731e+00	1.641547e+00	2.483821e+00	0.1046487
BWA	men	9.378920e-01	7.614955e-01	1.136710e+00	0.1030039
BWA	women	1.169874e+00	9.693681e-01	1.409736e+00	0.0958380
BWA	children	5.580395e-01	5.804150e-02	1.017538e+00	0.7181688
BWA	elderly	1.367949e+00	1.005567e+00	1.742552e+00	0.1408893
BRA	men	1.098896e+00	1.039891e+00	1.160838e+00	0.0279068
BRA	women	8.053534e-01	7.309247e-01	8.829873e-01	0.0486179
BRA	children	1.743574e+00	1.543135e+00	1.937767e+00	0.0582623
BRA	elderly	1.099973e+00	9.798241e-01	1.221955e+00	0.0562379
CMR	men	8.369225e-01	4.381949e-01	1.385915e+00	0.2913913
CMR	women	1.347360e+00	8.320907e-01	2.114714e+00	0.2341106
CMR	children	8.293000e-03	6.638400e-03	1.054499e+00	1.8258801
CMR	elderly	1.422903e+00	3.094842e-01	2.749634e+00	0.6680740
CAF	men		9.595200e-03	7.279761e-01	1.9257339
CAF	women		1.268497e+01	8.181859e+02	1.1181117
CAF	children	2.712579e+03	1.677898e+03	3.700503e+03	0.2306963
CAF	elderly		7.274097e+00	1.421827e+03	1.4995680
CHN	men	1.078229e+00	1.058838e+00	1.098451e+00	0.0093712
CHN	women	9.866479e-01	9.625331e-01	1.011289e+00	0.0125958
CHN	children	5.158000e-04	5.045000e-04	1.048200e-03	0.5988659
CHN	elderly	1.249818e+00	1.221533e+00	1.278209e+00	0.0115452
COG	men	9.660267e-01	7.189437e-01	1.265732e+00	0.1431177
COG	women	8.465459e-01	5.746739e-01	1.153060e+00	0.1806671
COG	children	8.139747e-01	2.638435e-01	1.385971e+00	0.4960815
COG	elderly	5.199500e-03	4.543300e-03	4.931879e-01	1.6612002
PRK	men	2.480758e+00	1.552197e+00	4.953659e+00	0.2978724
PRK	women	8.292000e-04	5.570000e-04	1.609200e-03	0.2734747
PRK	children	5.639600e-03	3.784900e-03	1.095670e-02	0.2783957
PRK	elderly	6.932600e-03	4.638000e-03	1.350250e-02	0.2745305
COD	men	8.746847e-01	7.445653e-01	1.020798e+00	0.0812312
COD	women	1.211986e+00	1.059529e+00	1.384901e+00	0.0684886
COD	children	6.301000e-01	3.823373e-01	8.867338e-01	0.2185283
COD	elderly	1.367422e+00	1.037898e+00	1.702424e+00	0.1259531
SWZ	men	3.096424e-01	7.454100e-03	3.894012e+00	2.0100504
SWZ	women	3.139535e+00	1.382589e+00	3.005383e+01	0.8418939
SWZ	children	1.660183e-01	8.735010e-02	1.493523e+00	0.7413539
SWZ	elderly	9.485113e+00	4.676456e+00	9.301572e+01	0.8280835
ETH	men
ETH	women
ETH	children
ETH	elderly		3.047626e+01	4.132809e+03	1.2530387
GAB	men	1.274433e+00	9.111950e-01	1.775929e+00	0.1683078
GAB	women	6.327534e-01	2.280833e-01	1.091246e+00	0.4560411
GAB	children	1.815480e+00	9.510292e-01	2.840146e+00	0.2799027
GAB	elderly	2.182018e+00	1.261462e+00	3.345034e+00	0.2494564
GIN	men	1.259288e+00	7.199042e-01	2.256353e+00	0.2936421
GIN	women	4.032861e-01	2.089500e-03	1.192335e+00	1.7644720
GIN	children	1.223883e+01	8.723926e+00	2.036576e+01	0.2180688
GIN	elderly	5.314341e-01	1.020960e-02	2.318303e+00	1.9010092
GNB	men
GNB	women
GNB	children	3.474931e+02	2.827332e+01	9.276847e+02	0.9096955
GNB	elderly	6.379640e+02	9.344468e+01	1.222367e+03	0.6807544
IND	men	1.045119e+00	1.037848e+00	1.052518e+00	0.0035559
IND	women	9.631582e-01	9.544110e-01	9.720959e-01	0.0046619
IND	children	1.449535e+00	1.433491e+00	1.465197e+00	0.0055723
IND	elderly	1.116824e+00	1.102207e+00	1.131984e+00	0.0067493
IDN	men	1.040249e+00	1.032163e+00	1.048425e+00	0.0039641
IDN	women	1.060099e+00	1.051169e+00	1.069076e+00	0.0043071
IDN	children	1.789678e+00	1.777922e+00	1.801433e+00	0.0033837
IDN	elderly	1.424596e+00	1.411127e+00	1.438136e+00	0.0048561
KAZ	men	2.499539e+00	2.152426e+00	2.958003e+00	0.0808195
KAZ	women	1.247400e-03	1.087600e-03	1.462400e-03	0.0757314
KAZ	children	3.814221e-01	1.553190e-02	1.224367e+00	1.5050375
KAZ	elderly	2.089417e+00	1.631628e+00	2.617447e+00	0.1191640
KEN	men	1.057610e+00	9.941846e-01	1.124127e+00	0.0315348
KEN	women	1.046212e+00	9.689061e-01	1.126316e+00	0.0384311
KEN	children	2.520780e+00	2.387248e+00	2.659032e+00	0.0276715
KEN	elderly	1.708269e+00	1.560234e+00	1.861918e+00	0.0451897
KGZ	men	1.033392e+00	9.201873e-01	1.154048e+00	0.0575366
KGZ	women	1.019332e+00	8.944801e-01	1.152769e+00	0.0647225
KGZ	children	1.060021e+00	7.536534e-01	1.342994e+00	0.1460420
KGZ	elderly	1.297232e+00	1.105398e+00	1.493438e+00	0.0766613
LSO	men	1.010852e+00	9.263644e-01	1.101422e+00	0.0439900
LSO	women	9.992451e-01	8.956028e-01	1.109863e+00	0.0539591
LSO	children	1.038281e+00	7.744667e-01	1.285143e+00	0.1309482
LSO	elderly	1.225958e+00	1.081702e+00	1.374302e+00	0.0610089
LBR	men	8.351083e-01	7.318541e-01	9.463800e-01	0.0662268
LBR	women	1.337110e+00	1.210855e+00	1.477033e+00	0.0504430
LBR	children	2.032738e+00	1.862616e+00	2.220550e+00	0.0443652
LBR	elderly	1.755939e+00	1.510203e+00	2.002534e+00	0.0715266
MWI	men	1.534511e+00	1.199224e+00	2.015969e+00	0.1326296
MWI	women	2.497918e-01	1.391800e-03	5.969550e-01	1.4991289
MWI	children	5.468000e-03	4.647000e-03	5.139829e-01	1.6831378
MWI	elderly	1.844736e+00	1.187221e+00	2.636526e+00	0.2039618
MNG	men	1.257279e+00	7.345181e-01	2.162088e+00	0.2726589
MNG	women	7.137662e-01	1.496807e-01	1.483691e+00	0.7038411
MNG	children	1.754440e-02	1.274690e-02	2.759240e-02	0.2053710
MNG	elderly	1.494329e+00	4.724860e-02	3.277424e+00	0.8570947
MMR	men	1.061325e+00	1.020939e+00	1.102387e+00	0.0194487
MMR	women	1.060742e+00	1.012176e+00	1.108461e+00	0.0232382
MMR	children	2.128624e+00	2.064252e+00	2.195362e+00	0.0157120
MMR	elderly	1.419964e+00	1.352353e+00	1.488525e+00	0.0245341
NAM	men	9.753666e-01	8.095270e-01	1.166815e+00	0.0936731
NAM	women	1.064649e+00	8.633918e-01	1.285497e+00	0.1016483
NAM	children	2.773110e-02	4.716000e-03	4.189158e-01	1.7444907
NAM	elderly	1.072356e+00	6.479664e-01	1.509509e+00	0.2150461
NPL	men	1.153839e+00	1.038729e+00	1.282789e+00	0.0536260
NPL	women	1.078744e+00	9.433265e-01	1.227046e+00	0.0671187
NPL	children	2.186500e-03	2.011800e-03	1.503520e-02	0.5506817
NPL	elderly	1.789510e+00	1.601906e+00	1.996224e+00	0.0562578
NGA	men
NGA	women
NGA	children	2.762353e+04	2.447995e+04	3.070231e+04	0.0577780
NGA	elderly
PAK	men	1.093061e+00	1.072304e+00	1.114775e+00	0.0099439
PAK	women	1.028330e+00	1.006678e+00	1.049349e+00	0.0106586
PAK	children	1.523971e+00	1.490532e+00	1.556594e+00	0.0109873
PAK	elderly	1.483934e+00	1.449548e+00	1.518920e+00	0.0119261
PER	men	1.147700e+00	1.087772e+00	1.211240e+00	0.0278942
PER	women	9.429502e-01	8.727436e-01	1.015790e+00	0.0389231
PER	children	1.118726e+00	9.449784e-01	1.279852e+00	0.0779012
PER	elderly	1.524757e+00	1.422869e+00	1.630718e+00	0.0346661
PHL	men	9.884333e-01	9.835886e-01	9.933821e-01	0.0025084
PHL	women	1.078733e+00	1.073021e+00	1.084435e+00	0.0026819
PHL	children	1.317005e+00	1.310259e+00	1.323784e+00	0.0026210
PHL	elderly	1.115657e+00	1.108294e+00	1.122876e+00	0.0033356
MDA	men	9.605597e-01	8.630048e-01	1.066169e+00	0.0544578
MDA	women	1.204476e+00	1.068065e+00	1.350488e+00	0.0594214
MDA	children	1.300455e+00	9.636368e-01	1.601503e+00	0.1295934
MDA	elderly	1.302405e+00	1.077582e+00	1.517302e+00	0.0868441
RUS	men	1.001505e+00	9.423225e-01	1.063315e+00	0.0309732
RUS	women	1.023065e+00	9.431722e-01	1.107517e+00	0.0408735
RUS	children	8.405560e-01	6.047032e-01	1.067565e+00	0.1449381
RUS	elderly	1.155550e+00	1.007961e+00	1.304383e+00	0.0651408
SLE	men	9.997773e-01	9.140730e-01	1.087635e+00	0.0442604
SLE	women	1.107794e+00	1.008758e+00	1.212633e+00	0.0467501
SLE	children	3.023323e+00	2.852832e+00	3.210868e+00	0.0298405
SLE	elderly	1.600556e+00	1.415687e+00	1.787016e+00	0.0599660
SOM	men	1.115260e+00	8.094591e-01	1.541186e+00	0.1631936
SOM	women	4.824254e-01	1.460468e-01	8.568661e-01	0.5683867
SOM	children	2.162383e+00	1.680425e+00	2.861485e+00	0.1355322
SOM	elderly	5.499100e-03	4.538100e-03	9.704480e-02	0.6390826
ZAF	men	6.073673e+03	4.522644e+03	7.616791e+03	0.1323932
ZAF	women
ZAF	children
ZAF	elderly	2.238414e+05	2.219424e+05	2.256700e+05	0.0042179
TJK	men	1.128918e+00	1.021394e+00	1.251659e+00	0.0519725
TJK	women	8.160715e-01	6.981792e-01	9.388207e-01	0.0755319
TJK	children	1.445930e+00	1.209357e+00	1.680109e+00	0.0831249
TJK	elderly	9.235389e-01	6.916223e-01	1.145862e+00	0.1311484
THA	men	2.395921e+00	2.211708e+00	2.615406e+00	0.0426967
THA	women	2.433617e+00	2.236708e+00	2.666848e+00	0.0446537
THA	children	2.953137e+00	2.455125e+00	3.474020e+00	0.0880277
THA	elderly	6.030829e-01	4.506799e-01	7.636014e-01	0.1345298
UKR	men	1.034024e+00	9.890829e-01	1.081966e+00	0.0228552
UKR	women	9.594591e-01	8.993702e-01	1.021710e+00	0.0323397
UKR	children	1.308429e+00	1.130200e+00	1.482813e+00	0.0695838
UKR	elderly	1.149581e+00	1.046695e+00	1.248807e+00	0.0450825
TZA	men	1.255679e+01	7.655100e-03	1.734684e+03	3.3546704
TZA	women	3.202580e-02	2.045900e-03	1.363528e-01	1.1047915
TZA	children	5.547642e+01	3.243295e+00	5.541607e+03	2.9221564
TZA	elderly	7.626510e-02	4.865400e-03	3.399913e-01	1.1084180
UZB	men	8.863624e-01	7.926697e-01	9.893216e-01	0.0561977
UZB	women	1.183579e+00	1.075531e+00	1.300900e+00	0.0490086
UZB	children	1.008920e+00	8.421496e-01	1.178685e+00	0.0853631
UZB	elderly	1.160510e+00	1.002285e+00	1.322466e+00	0.0708210
VNM	men	5.692000e-04	3.421000e-04	1.634000e-03	0.4352158
VNM	women	6.679130e+00	3.885495e+00	1.986806e+01	0.5260317
VNM	children	1.365064e+01	7.305995e+00	4.131188e+01	0.5439234
VNM	elderly	7.306412e-01	1.554300e-03	3.420276e+00	2.3039490
ZMB	men
ZMB	women
ZMB	children
ZMB	elderly
ZWE	men	1.028717e+00	9.741936e-01	1.085173e+00	0.0275293
ZWE	women	1.095592e+00	1.032814e+00	1.162013e+00	0.0298343
ZWE	children	4.443576e-01	2.791920e-01	6.019062e-01	0.1975437
ZWE	elderly	1.509386e+00	1.395402e+00	1.620852e+00	0.0382639

ga<-plotFun(resa)
# print(ga$ridgePlot)
# Prep data for comparison to children meta-analysis
resa1<-as.data.frame(resa$compDat)
resac1<-resa1[resa1$group=="children",]
resac1$Region<-dat[year=="2020",g_whoregion]
resac1$Country<-dat[year=="2020",country]
resac1$observed_A<-as.data.frame(resa$reference[,3])
resac1$expected_A<-as.data.frame(resa$reference[,4])
# Remove RR of Na
resac1<-subset(resac1,!is.na(RR))
# Conduct meta-analysis
Risk_children<-metagen(studlab=Country,sm="RR",TE=log(RR),subgroup=Region,fixed=FALSE,data=resac1,backtransf=TRUE,lower=log(RR_low),upper=log(RR_upp))
# Countries with evidence of effect
AC_evidence_hi_strong<-sum(exp(Risk_children$TE)>1.1&(Risk_children$pval<0.01))
AC_evidence_hi_med<-sum(exp(Risk_children$TE)>1.1&Risk_children$pval>0.01&Risk_children$pval<0.05)
AC_evidence_hi_weak<-sum(exp(Risk_children$TE)>1.25&Risk_children$pval>0.05&Risk_children$pval<0.1)
AC_evidence_lo_strong<-sum(exp(Risk_children$TE)<1/1.1&(Risk_children$pval<0.01))
AC_evidence_lo_med<-sum(exp(Risk_children$TE)<1/1.1&Risk_children$pval>0.01&Risk_children$pval<0.05)
AC_evidence_lo_weak<-sum(exp(Risk_children$TE)<1/1.25&Risk_children$pval>0.05&Risk_children$pval<0.1)
# P-value for regional subgroups
#Risk_children$pval.random.w
print("Meta-analysis for children/adults")

## [1] "Meta-analysis for children/adults"

Risk_children

## Number of studies combined: k = 42
## 
##                          RR           95%-CI    z p-value
## Random effects model 1.0876 [0.4062; 2.9120] 0.17  0.8672
## 
## Quantifying heterogeneity:
##  tau^2 = 10.3452 [6.9616; 17.2250]; tau = 3.2164 [2.6385; 4.1503]
##  I^2 = 99.9%; H = 31.41 [30.65; 32.19]
## 
## Test of heterogeneity:
##         Q d.f. p-value
##  40443.56   41       0
## 
## Results for subgroups (random effects model):
##                                         k     RR            95%-CI   tau^2
## Region = European Region                9 1.3250 [1.0432;  1.6830]  0.1037
## Region = South-East Asia Region         7 0.3125 [0.0327;  2.9861]  9.2356
## Region = African Region                18 2.5985 [0.4051; 16.6659] 15.6553
## Region = Region of the Americas         2 1.4008 [0.9068;  2.1637]  0.0938
## Region = Western Pacific Region         4 0.1120 [0.0013;  9.4285] 20.3942
## Region = Eastern Mediterranean Region   2 1.7684 [1.2599;  2.4821]  0.0519
##                                          tau        Q   I^2
## Region = European Region              0.3221    56.13 85.7%
## Region = South-East Asia Region       3.0390  2134.86 99.7%
## Region = African Region               3.9567 24984.67 99.9%
## Region = Region of the Americas       0.3062    21.03 95.2%
## Region = Western Pacific Region       4.5160  2276.17 99.9%
## Region = Eastern Mediterranean Region 0.2279     6.60 84.8%
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   5.31    5  0.3789
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau

# Sensitivity analysis removing countries with more cases than expected
resac1s<-resac1[resac1$moreObsThanExp=="no"&(resac1$expected_A>resac1$observed_A),]
Risk_childrenS<-metagen(studlab=Country,sm="RR",TE=log(RR),subgroup=Region,fixed=FALSE,data=resac1s,backtransf=TRUE,lower=log(RR_low),upper=log(RR_upp))
print("Sensitivity analysis for children/adults removing countries with more cases than expected")

## [1] "Sensitivity analysis for children/adults removing countries with more cases than expected"

Risk_childrenS

## Number of studies combined: k = 32
## 
##                          RR           95%-CI    z p-value
## Random effects model 1.5713 [1.2174; 2.0280] 3.47  0.0005
## 
## Quantifying heterogeneity:
##  tau^2 = 0.4701 [0.3693; 1.6553]; tau = 0.6857 [0.6077; 1.2866]
##  I^2 = 99.6% [99.5%; 99.6%]; H = 15.19 [14.49; 15.94]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  7155.54   31       0
## 
## Results for subgroups (random effects model):
##                                         k     RR            95%-CI  tau^2
## Region = European Region                9 1.3250 [1.0432;  1.6830] 0.1037
## Region = South-East Asia Region         5 1.8085 [1.3481;  2.4262] 0.1107
## Region = African Region                12 1.3639 [0.6543;  2.8430] 1.4432
## Region = Region of the Americas         2 1.4008 [0.9068;  2.1637] 0.0938
## Region = Eastern Mediterranean Region   2 1.7684 [1.2599;  2.4821] 0.0519
## Region = Western Pacific Region         2 4.0666 [0.4117; 40.1637] 2.6364
##                                          tau       Q   I^2
## Region = European Region              0.3221   56.13 85.7%
## Region = South-East Asia Region       0.3328 1523.90 99.7%
## Region = African Region               1.2013  310.89 96.5%
## Region = Region of the Americas       0.3062   21.03 95.2%
## Region = Eastern Mediterranean Region 0.2279    6.60 84.8%
## Region = Western Pacific Region       1.6237   27.99 96.4%
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   4.28    5  0.5092
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau

# Sensitivity analysis removing countries with poor fit or limited data
resac1p<-resac1[!resac1$iso3%in%c("AZE","BRA","CMR","COD","CAF","COG","PRK","GAB","GNB","LBR","SLE","KEN","LSO","MWI","MNG","MMR","NPL","NGA","PAK","PHL","SOM","TJK","THA","UKR","VNM","ZMB"),]
Risk_childrenP<-metagen(studlab=Country,sm="RR",TE=log(RR),subgroup=Region,fixed=FALSE,data=resac1p,backtransf=TRUE,lower=log(RR_low),upper=log(RR_upp))
print("Sensitivity analysis for children/adults removing countries with a poor fit or limited data")

## [1] "Sensitivity analysis for children/adults removing countries with a poor fit or limited data"

Risk_childrenP

## Number of studies combined: k = 17
## 
##                          RR           95%-CI     z p-value
## Random effects model 0.6534 [0.2114; 2.0197] -0.74  0.4599
## 
## Quantifying heterogeneity:
##  tau^2 = 5.2962 [2.8354; 13.2897]; tau = 2.3014 [1.6839; 3.6455]
##  I^2 = 99.5% [99.5%; 99.6%]; H = 14.61 [13.64; 15.64]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  3412.97   16       0
## 
## Results for subgroups (random effects model):
##                                   k     RR           95%-CI  tau^2    tau
## Region = South-East Asia Region   3 1.4709 [1.1862; 1.8239] 0.0360 0.1897
## Region = European Region          6 1.1939 [0.8313; 1.7146] 0.1504 0.3878
## Region = African Region           6 0.8315 [0.1114; 6.2032] 5.4874 2.3425
## Region = Western Pacific Region   1 0.0005 [0.0004; 0.0007]     --     --
## Region = Region of the Americas   1 1.1187 [0.9613; 1.3019]     --     --
##                                       Q   I^2
## Region = South-East Asia Region 1268.21 99.8%
## Region = European Region          21.47 76.7%
## Region = African Region          159.08 96.9%
## Region = Western Pacific Region    0.00    --
## Region = Region of the Americas    0.00    --
## 
## Test for subgroup differences (random effects model):
##                        Q d.f. p-value
## Between groups   1577.25    4       0
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau

# Prep data for comparison to elderly meta-analysis
resae1<-resa1[resa1$group=="elderly",]
resae1$Region<-dat[year=="2020",g_whoregion]
resae1$Country<-dat[year=="2020",country]
resae1$observed_A<-as.data.frame(resa$reference[,3])
resae1$expected_A<-as.data.frame(resa$reference[,4])
# Remove RR of Na
resae1<-subset(resae1,!is.na(RR))
# Conduct meta-analysis
Risk_elderly<-metagen(studlab=Country,sm="RR",TE=log(RR),subgroup=Region,fixed=FALSE,data=resae1,backtransf=TRUE,lower=log(RR_low),upper=log(RR_upp))
# Countries with evidence of effect
AE_evidence_hi_strong<-sum(exp(Risk_elderly$TE)>1.1&(Risk_elderly$pval<0.01))
AE_evidence_hi_med<-sum(exp(Risk_elderly$TE)>1.1&Risk_elderly$pval>0.01&Risk_elderly$pval<0.05)
AE_evidence_hi_weak<-sum(exp(Risk_elderly$TE)>1.25&Risk_elderly$pval>0.05&Risk_elderly$pval<0.1)
AE_evidence_lo_strong<-sum(exp(Risk_elderly$TE)<1/1.1&(Risk_elderly$pval<0.01))
AE_evidence_lo_med<-sum(exp(Risk_elderly$TE)<1/1.1&Risk_elderly$pval>0.01&Risk_elderly$pval<0.05)
AE_evidence_lo_weak<-sum(exp(Risk_elderly$TE)<1/1.25&Risk_elderly$pval>0.05&Risk_elderly$pval<0.1)
# P-value for regional subgroups
#Risk_elderly$pval.random.w
print("Meta-analysis for elderly/adults")

## [1] "Meta-analysis for elderly/adults"

Risk_elderly

## Number of studies combined: k = 41
## 
##                          RR           95%-CI    z p-value
## Random effects model 1.4036 [0.6226; 3.1643] 0.82  0.4137
## 
## Quantifying heterogeneity:
##  tau^2 = 6.8181 [4.5375; 11.4171]; tau = 2.6111 [2.1301; 3.3789]
##  I^2 = 100.0%; H = 395.22 [393.91; 396.54]
## 
## Test of heterogeneity:
##           Q d.f. p-value
##  6248047.29   40       0
## 
## Results for subgroups (random effects model):
##                                         k     RR            95%-CI   tau^2
## Region = European Region                9 1.3312 [1.1281;  1.5709]  0.0554
## Region = South-East Asia Region         7 0.5815 [0.1369;  2.4702]  3.7993
## Region = African Region                17 2.9039 [0.5051; 16.6949] 13.2053
## Region = Region of the Americas         2 1.2990 [0.9433;  1.7888]  0.0511
## Region = Western Pacific Region         4 1.1805 [1.0570;  1.3184]  0.0063
## Region = Eastern Mediterranean Region   2 0.0954 [0.0004; 22.9895] 15.3627
##                                          tau         Q    I^2
## Region = European Region              0.2354     51.29  84.4%
## Region = South-East Asia Region       1.9492   1327.15  99.5%
## Region = African Region               3.6339 286543.88 100.0%
## Region = Region of the Americas       0.2261     24.33  95.9%
## Region = Western Pacific Region       0.0794     89.06  96.6%
## Region = Eastern Mediterranean Region 3.9195     51.32  98.1%
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   4.31    5  0.5056
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau

# Sensitivity analysis removing countries with more cases than expected
resae1s<-resae1[resae1$moreObsThanExp=="no"&(resae1$expected_A>resae1$observed_A),]
Risk_elderlyS<-metagen(studlab=Country,sm="RR",TE=log(RR),subgroup=Region,fixed=FALSE,data=resae1s,backtransf=TRUE,lower=log(RR_low),upper=log(RR_upp))
print("Sensitivity analysis for elderly/adults removing countries with more cases than expected")

## [1] "Sensitivity analysis for elderly/adults removing countries with more cases than expected"

Risk_elderlyS

## Number of studies combined: k = 35
## 
##                          RR           95%-CI    z  p-value
## Random effects model 1.3594 [1.2508; 1.4775] 7.22 < 0.0001
## 
## Quantifying heterogeneity:
##  tau^2 = 0.0470 [0.0293; 0.1264]; tau = 0.2168 [0.1711; 0.3555]
##  I^2 = 98.7% [98.5%; 98.8%]; H = 8.66 [8.10; 9.26]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  2548.12   34       0
## 
## Results for subgroups (random effects model):
##                                         k     RR           95%-CI  tau^2    tau
## Region = European Region                9 1.3312 [1.1281; 1.5709] 0.0554 0.2354
## Region = South-East Asia Region         6 1.2164 [0.9221; 1.6048] 0.1165 0.3414
## Region = African Region                13 1.5325 [1.3898; 1.6898] 0.0143 0.1197
## Region = Region of the Americas         2 1.2990 [0.9433; 1.7888] 0.0511 0.2261
## Region = Western Pacific Region         4 1.1805 [1.0570; 1.3184] 0.0063 0.0794
## Region = Eastern Mediterranean Region   1 1.4839 [1.4497; 1.5190]     --     --
##                                            Q   I^2
## Region = European Region               51.29 84.4%
## Region = South-East Asia Region       957.78 99.5%
## Region = African Region                36.70 67.3%
## Region = Region of the Americas        24.33 95.9%
## Region = Western Pacific Region        89.06 96.6%
## Region = Eastern Mediterranean Region   0.00    --
## 
## Test for subgroup differences (random effects model):
##                      Q d.f. p-value
## Between groups   20.18    5  0.0012
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau

# Sensitivity analysis removing countries with poor fit or limited data
resae1p<-resae1[!resae1$iso3%in%c("CMR","CAF","COD","LBR","SLE","PRK","GAB","GNB","KEN","IND","MWI","MNG","MMR","NAM","NPL","NGA","PAK","MDA","SOM","ZAF","UZB","VNM","ZMB","ZWE"),]
Risk_elderlyP<-metagen(studlab=Country,sm="RR",TE=log(RR),subgroup=Region,fixed=FALSE,data=resae1p,backtransf=TRUE,lower=log(RR_low),upper=log(RR_upp))
print("Sensitivity analysis for elderly/adults removing countries with a poor fit or limited data")

## [1] "Sensitivity analysis for elderly/adults removing countries with a poor fit or limited data"

Risk_elderlyP

## Number of studies combined: k = 20
## 
##                          RR           95%-CI    z p-value
## Random effects model 1.2375 [1.0792; 1.4190] 3.05  0.0023
## 
## Quantifying heterogeneity:
##  tau^2 = 0.0727 [0.2591; 3.5088]; tau = 0.2696 [0.5090; 1.8732]
##  I^2 = 99.0% [98.8%; 99.2%]; H = 10.01 [9.23; 10.85]
## 
## Test of heterogeneity:
##        Q d.f. p-value
##  1902.14   19       0
## 
## Results for subgroups (random effects model):
##                                   k     RR           95%-CI  tau^2    tau
## Region = European Region          7 1.3661 [1.1001; 1.6964] 0.0757 0.2752
## Region = South-East Asia Region   3 1.0322 [0.6236; 1.7086] 0.1926 0.4389
## Region = African Region           6 0.4474 [0.0590; 3.3928] 5.6347 2.3738
## Region = Region of the Americas   2 1.2990 [0.9433; 1.7888] 0.0511 0.2261
## Region = Western Pacific Region   2 1.1802 [1.0559; 1.3191] 0.0064 0.0798
##                                      Q   I^2
## Region = European Region         49.84 88.0%
## Region = South-East Asia Region 174.70 98.9%
## Region = African Region          35.59 86.0%
## Region = Region of the Americas  24.33 95.9%
## Region = Western Pacific Region  88.94 98.9%
## 
## Test for subgroup differences (random effects model):
##                     Q d.f. p-value
## Between groups   2.89    4  0.5762
## 
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau

10. Plot missing cases

We plot country-level observed vs expected cases

# Observed vs expected notifications for men
options(ggrepel.max.overlaps = 12)
resmm1 <- merge(as.data.frame(resm$reference),resm1[c("iso3","Region")],by="iso3")
plot_Mmissing<-ggplot(data=resmm1,aes(x=log10(observed),y=log10(expected),color=Region,label=iso3))+
  geom_text_repel(aes(label=iso3))+
  geom_point(size=4)+
  geom_abline(intercept=0,slope=1,linetype="dashed",colour="black",size=0.5)+
  geom_hline(yintercept=0, linetype="solid", colour="black", size=0.1)+
  geom_vline(xintercept=0, linetype="solid", colour="black", size=0.1)+
  scale_colour_manual(values=c("#79ACE7","#E8E863","#E179E7","#E77979","#9EE779","#7BE7CF"),guide="none")+
  scale_y_continuous(trans='log10',name="Expected men",breaks=c(3,4,5,6),labels=c("1,000","10,000","100,000","1,000,000"))+
  scale_x_continuous(trans='log10',name="Observed men",breaks=c(3,4,5,6),labels=c("1,000","10,000","100,000","1,000,000"))+
  coord_cartesian(xlim =c(3,6.5),ylim =c(3,6.5))+
  theme(axis.text.x=element_text(size=10),axis.text.y=element_text(size=10,angle=90,hjust=0.5),axis.title.x=element_text(size=12),axis.title.y=element_text(size=12),legend.position=c(0.8,0.3),legend.background = element_blank(),legend.text = element_text(size=6),legend.title = element_text(size=9))
plot_Mmissing

ggsave("Missing_M.png",device="png",width=10,height=10,units=c("cm"))
# Observed vs expected notifications for women
options(ggrepel.max.overlaps = 12)
plot_Wmissing<-ggplot(data=resm1,aes(x=log10(observed),y=log10(expected),color=Region,label=iso3))+
  geom_text_repel(aes(label=iso3))+
  geom_point(size=4)+
  geom_abline(intercept=0,slope=1,linetype="dashed",colour="black",size=0.5)+
  geom_hline(yintercept=0, linetype="solid", colour="black", size=0.1)+
  geom_vline(xintercept=0, linetype="solid", colour="black", size=0.1)+
  scale_colour_manual(values=c("#79ACE7","#E8E863","#E179E7","#E77979","#9EE779","#7BE7CF"))+
  scale_y_continuous(trans='log10',name="Expected women",breaks=c(2,4,6),labels=c("100","10,000","1,000,000"))+
  scale_x_continuous(trans='log10',name="Observed women",breaks=c(2,4,6),labels=c("100","10,000","1,000,000"))+
  coord_cartesian(xlim =c(2,6.5),ylim =c(2,6.5))+
  theme(axis.text.x=element_text(size=10),axis.text.y=element_text(size=10,angle=90,hjust=0.5),axis.title.x=element_text(size=12),axis.title.y=element_text(size=12),legend.position=c(0.8,0.3),legend.background = element_blank(),legend.text = element_text(size=6),legend.title = element_text(size=9))
plot_Wmissing

ggsave("Missing_W.png",device="png",width=10,height=10,units=c("cm"))
# Observed vs expected notifications for children
options(ggrepel.max.overlaps = 20)
plot_Cmissing<-ggplot(data=resac1,aes(x=log10(observed),y=log10(expected),color=Region,label=iso3))+
  geom_text_repel(aes(label=iso3))+
  geom_point(size=4)+
  geom_abline(intercept=0,slope=1,linetype="dashed",colour="black",size=0.5)+
  scale_colour_manual(values=c("#79ACE7","#E8E863","#E179E7","#E77979","#9EE779","#7BE7CF"),guide="none")+
  scale_y_continuous(trans='log10',name="Expected children",breaks=c(1,3,5),labels=c("10","1,000","100,000"))+
  scale_x_continuous(trans='log10',name="Observed children",breaks=c(1,3,5),labels=c("10","1,000","100,000"))+
  coord_cartesian(xlim =c(0.5,5.5),ylim =c(0.5,5.5))+
  theme(axis.text.x=element_text(size=10),axis.text.y=element_text(size=10,angle=90,hjust=0.5),axis.title.x=element_text(size=12),axis.title.y=element_text(size=12),legend.position=c(0.8,0.3),legend.background = element_blank(),legend.text = element_text(size=6),legend.title = element_text(size=9))
plot_Cmissing

ggsave("Missing_C.png",device="png",width=10,height=10,units=c("cm"))
# Observed vs expected notifications for elderly
options(ggrepel.max.overlaps = 15)
plot_Emissing<-ggplot(data=resae1,aes(x=log10(observed),y=log10(expected),color=Region,label=iso3))+
  geom_text_repel(aes(label=iso3))+
  geom_point(size=4)+
  geom_abline(intercept=0,slope=1,linetype="dashed",colour="black",size=0.5)+
  geom_hline(yintercept=0, linetype="solid", colour="black", size=0.1)+
  geom_vline(xintercept=0, linetype="solid", colour="black", size=0.1)+
  scale_colour_manual(values=c("#79ACE7","#E8E863","#E179E7","#E77979","#9EE779","#7BE7CF"),guide="none")+
  scale_y_continuous(trans='log10',name="Expected elderly",breaks=c(2,3,4,5),labels=c("100","1,000","10,000","100,000"))+
  scale_x_continuous(trans='log10',name="Observed elderly",breaks=c(2,3,4,5),labels=c("100","1,000","10,000","100,000"))+
  coord_cartesian(xlim =c(1.5,5.5),ylim =c(1.5,5.5))+
  theme(axis.text.x=element_text(size=10),axis.text.y=element_text(size=10,angle=90,hjust=0.5),axis.title.x=element_text(size=12),axis.title.y=element_text(size=12),legend.position=c(0.8,0.3),legend.background = element_blank(),legend.text = element_text(size=6),legend.title = element_text(size=9))
plot_Emissing

ggsave("Missing_E.png",device="png",width=10,height=10,units=c("cm"))
# Observed vs expected notifications for adults
resaa1 <- merge(as.data.frame(resa$reference),resac1[c("iso3","Region")],by="iso3")
options(ggrepel.max.overlaps = 15)
plot_Amissing<-ggplot(data=resaa1,aes(x=log10(observed),y=log10(expected),color=Region,label=iso3))+
  geom_text_repel(aes(label=iso3))+
  geom_point(size=4)+
  geom_abline(intercept=0,slope=1,linetype="dashed",colour="black",size=0.5)+
  geom_hline(yintercept=0, linetype="solid", colour="black", size=0.1)+
  geom_vline(xintercept=0, linetype="solid", colour="black", size=0.1)+
  scale_colour_manual(values=c("#79ACE7","#E8E863","#E179E7","#E77979","#9EE779","#7BE7CF"),guide="none")+
  scale_y_continuous(trans='log10',name="Expected adults",breaks=c(3,4,5,6),labels=c("1,000","10,000","100,000","1,000,000"))+
  scale_x_continuous(trans='log10',name="Observed adults",breaks=c(3,4,5,6),labels=c("1,000","10,000","100,000","1,000,000"))+
  coord_cartesian(xlim =c(3,6.5),ylim =c(3,6.5))+
  theme(axis.text.x=element_text(size=10),axis.text.y=element_text(size=10,angle=90,hjust=0.5),axis.title.x=element_text(size=12),axis.title.y=element_text(size=12),legend.position=c(0.8,0.3),legend.background = element_blank(),legend.text = element_text(size=6),legend.title = element_text(size=9))
plot_Amissing

ggsave("Missing_A.png",device="png",width=10,height=10,units=c("cm"))

11. Plot meta-analyses

We plot forest plots for the meta-analyses

# Men to women comparison
png("forest_MF.png", width = 1000, height = 1800, res=120) 
forest(x=Risk_sex, print.subgroup.name=FALSE,text.random="Overall summary",text.random.w="Regional summary",ref=1,at=(c(.1, 1, 10)),
       leftcols=c("studlab"),rightcols=c("effect","ci"),weight.study="same",weight.subgroup="same",test.subgroup.random=FALSE,xlim=c(0.1,10),
       lty.random = 0,hetstat=FALSE,sortvar=-TE,leftlabs = c("Country"),smlab=("Women:Men"),rightlabs = c("Risk ratio","95% CI"),
       col.study="black",col.diamond = "white",col.square="black",col.inside="black",squaresize=0.5,col.by="black",allstudies=FALSE)
dev.off()

## png 
##   2

# Adults to children comparison
png("forest_AC.png", width = 1000, height = 1800, res=120) 
forest(x=Risk_children, backtransf=TRUE,print.subgroup.name=FALSE,text.random="Overall summary",text.random.w="Regional summary",ref=1,at=(c(.1, 1, 10)),
       leftcols=c("studlab"),rightcols=c("effect","ci"),weight.study="same",weight.subgroup="same",test.subgroup.random=FALSE,xlim=c(0.1,10),
       lty.random = 0,hetstat=FALSE,sortvar=-TE,leftlabs = c("Country"),smlab=("Children:Adults"),rightlabs = c("Risk ratio","95% CI"),
       col.study="black",col.diamond = "white",col.square="black",col.inside="black",squaresize=0.5,col.by="black",allstudies=FALSE)
dev.off()

## png 
##   2

# Adults to elderly comparison
png("forest_AE.png", width = 1000, height = 1800, res=120) 
forest(x=Risk_elderly, backtransf=TRUE,print.subgroup.name=FALSE,text.random="Overall summary",text.random.w="Regional summary",ref=1,at=(c(.1, 1, 10)),
       leftcols=c("studlab"),rightcols=c("effect","ci"),weight.study="same",weight.subgroup="same",test.subgroup.random=FALSE,xlim=c(0.1,10),
       lty.random = 0,hetstat=FALSE,sortvar=-TE,leftlabs = c("Country"),smlab=("Elderly:Adults"),rightlabs = c("Risk ratio","95% CI"),
       col.study="black",col.diamond = "white",col.square="black",col.inside="black",squaresize=0.5,col.by="black",allstudies=FALSE)
dev.off() 

## png 
##   2