{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T04:12:58Z","timestamp":1788581578961,"version":"build-2803163510"},"reference-count":36,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100015666","name":"Aksum University","doi-asserted-by":"publisher","award":["AKUR\/CNCSM\/006\/16"],"award-info":[{"award-number":["AKUR\/CNCSM\/006\/16"]}],"id":[{"id":"10.13039\/100015666","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["INQUIRY"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Tigray experienced a devastating armed conflict in November 2020 that systematically dismantled one of the country\u2019s best-performing health systems, with only 30% of hospitals and 17% of health centers remaining functional six months into the war. This study aimed to compare several count data models in analyzing postwar healthcare services utilization disparity and associated factors in Eastern Tigray, Ethiopia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Repeated cross-sectional analytical study design employed secondary data from 20 facilities across 18 districts of Eastern Tigray comprising 2,040 observations. We compared many models and ZINB-GLMM was selected to handle the healthcare utilization with substantially excess zeros, unmeasured heterogeneity of facility, and potential period-specific interaction. The model considered demographic, service-related, geographic, and conflict-period factors. Model performance was compared using AIC and BIC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Mean utilization declined from 335 recipients prewar to 41 postwar, an 87.8% reduction. The prewar period was significantly associated with higher rate of utilization compared with the postwar period (\n                      <jats:inline-formula>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                          <mml:mrow>\n                            <mml:mi>p<\/mml:mi>\n                            <mml:mo>&lt;<\/mml:mo>\n                            <mml:mo>\u2009<\/mml:mo>\n                            <mml:mn>0.001<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:inline-formula>\n                      ). The ZINB-GLMM outperformed (AIC=15,772.66, BIC=16,109.90), with the moderate variability of random intercept (\n                      <jats:inline-formula>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                          <mml:mrow>\n                            <mml:msubsup>\n                              <mml:mi>\u03c3<\/mml:mi>\n                              <mml:mrow>\n                                <mml:mi>r<\/mml:mi>\n                                <mml:mi>i<\/mml:mi>\n                              <\/mml:mrow>\n                              <mml:mn>2<\/mml:mn>\n                            <\/mml:msubsup>\n                            <mml:mo>=<\/mml:mo>\n                            <mml:mn>0.16<\/mml:mn>\n                            <mml:mo>,<\/mml:mo>\n                            <mml:msub>\n                              <mml:mi>\u03c3<\/mml:mi>\n                              <mml:mrow>\n                                <mml:mi>r<\/mml:mi>\n                                <mml:mi>i<\/mml:mi>\n                              <\/mml:mrow>\n                            <\/mml:msub>\n                            <mml:mo>=<\/mml:mo>\n                            <mml:mn>0.4<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:inline-formula>\n                      ) and\n                      <jats:inline-formula>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                          <mml:mrow>\n                            <mml:mo>(<\/mml:mo>\n                            <mml:mrow>\n                              <mml:mi>I<\/mml:mi>\n                              <mml:mi>C<\/mml:mi>\n                              <mml:mi>C<\/mml:mi>\n                              <mml:mo>=<\/mml:mo>\n                              <mml:mn>0.970<\/mml:mn>\n                              <mml:mo>,<\/mml:mo>\n                              <mml:mn>95<\/mml:mn>\n                              <mml:mo>%<\/mml:mo>\n                              <mml:mo>\u2009<\/mml:mo>\n                              <mml:mi>C<\/mml:mi>\n                              <mml:mi>I<\/mml:mi>\n                              <mml:mo>:<\/mml:mo>\n                              <mml:mn>0.908<\/mml:mn>\n                              <mml:mo>\u2013<\/mml:mo>\n                              <mml:mn>0.986<\/mml:mn>\n                            <\/mml:mrow>\n                            <mml:mo>)<\/mml:mo>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:inline-formula>\n                      indicates significant between-facility proportion of variability (97.0%) in healthcare utilization attributable to differences between facilities after accounting for fixed effects. The zero-inflation component of the model was crucial and the prewar period was significantly associated with lower odds of excess zero counts relative to the postwar period (\n                      <jats:inline-formula>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                          <mml:mrow>\n                            <mml:mi>p<\/mml:mi>\n                            <mml:mo>&lt;<\/mml:mo>\n                            <mml:mo>\u2009<\/mml:mo>\n                            <mml:mn>0.001<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:inline-formula>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The armed war produced terrible declines in healthcare utilization with noticeable geospatial disparities. ZINB-GLMM substantially outperformed, confirming its appropriateness for overdispersion, excess zeros, unmeasured heterogeneity for clustering of facilities, and period-specific interaction effects. The findings underline the significance of strengthening resilient reproductive health systems and ensuring continuity of essential maternal health services during humanitarian crises and post-conflict recovery periods. Recovery efforts must prioritize rural and peripheral areas while rebuilding reproductive health services.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00469580261486803","type":"journal-article","created":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T23:43:33Z","timestamp":1788392613000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Postwar Decline in Healthcare Services Utilization With Rising Disparities: A Repeated Cross-Sectional Study Using Count Data Models"],"prefix":"10.1177","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1347-6994","authenticated-orcid":false,"given":"Yemane Hailu","family":"Fissuh","sequence":"first","affiliation":[{"name":"Aksum University"},{"name":"Aksum University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yemane Asmelash","family":"Gebretensae","sequence":"additional","affiliation":[{"name":"Aksum University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tesfay Hailu","family":"Shifarre","sequence":"additional","affiliation":[{"name":"Aksum University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mebrihit Berihu","family":"Mesfun","sequence":"additional","affiliation":[{"name":"Aksum University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,2]]},"reference":[{"key":"e_1_3_8_2_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.324.7333.346"},{"key":"e_1_3_8_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(09)61926-7"},{"key":"e_1_3_8_4_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjgh-2021-007328"},{"key":"e_1_3_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.socscimed.2009.09.032"},{"key":"e_1_3_8_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(21)00825-4"},{"key":"e_1_3_8_7_2","volume-title":"Ethiopia Mini Demographic and Health Survey 2019: Key Indicators","author":"Ethiopian Public Health Institute, ICF","year":"2019","unstructured":"Ethiopian Public Health Institute, ICF. Ethiopia Mini Demographic and Health Survey 2019: Key Indicators. Rockville, Maryland, USA: EPHI and ICF; 2019."},{"key":"e_1_3_8_8_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12992-019-0470-1"},{"key":"e_1_3_8_9_2","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6963-12-352"},{"issue":"4","key":"e_1_3_8_10_2","first-page":"8038","article-title":"The Effect of War on Health Institutions of Eastern Tigray Zone, Tigray State, Ethiopia","volume":"8","author":"Tekulu FB","year":"2023","unstructured":"Tekulu FB, Gebre HT, Hailu HG, et al. The Effect of War on Health Institutions of Eastern Tigray Zone, Tigray State, Ethiopia. J Health Commun. 2023;8(4):8038.","journal-title":"J Health Commun"},{"key":"e_1_3_8_11_2","volume-title":"Peace Agreements Database","author":"African Union","year":"2022","unstructured":"African Union. Government of the Federal Democratic Republic of Ethiopia and Tigray People\u2019s Liberation Front. Agreement for Lasting Peace through a Permanent Cessation of Hostilities between the Government of the Federal Democratic Republic of Ethiopia and the Tigray People\u2019s Liberation Front. In: Peace Agreements Database; 2 Nov. 2022. www.peaceagreements.org\/agreements\/wgg\/2443\/"},{"key":"e_1_3_8_12_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-024-20314-1"},{"key":"e_1_3_8_13_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0336121"},{"issue":"1","key":"e_1_3_8_14_2","first-page":"1","article-title":"Prevalence and Factors Associated with Modern Family Planning Methods Utilization among Married Women in Post-War Tigray, Ethiopia","volume":"35","author":"Gebremariam AT","year":"2025","unstructured":"Gebremariam AT, Abreha GF, Gebre-Egziabher KA. Prevalence and Factors Associated with Modern Family Planning Methods Utilization among Married Women in Post-War Tigray, Ethiopia. Ethiop J Health Sci. 2025;35(1):1-11","journal-title":"Ethiop J Health Sci"},{"key":"e_1_3_8_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01381-y"},{"key":"e_1_3_8_16_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjgh-2020-003317"},{"key":"e_1_3_8_17_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139013567"},{"key":"e_1_3_8_18_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511973420"},{"key":"e_1_3_8_19_2","doi-asserted-by":"publisher","DOI":"10.2174\/1874944502013010547"},{"key":"e_1_3_8_20_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgph.0001015"},{"key":"e_1_3_8_21_2","doi-asserted-by":"publisher","DOI":"10.1890\/07-0043.1"},{"key":"e_1_3_8_22_2","doi-asserted-by":"publisher","DOI":"10.2307\/3314912"},{"key":"e_1_3_8_23_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12884-015-0449-8"},{"key":"e_1_3_8_24_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-022-01685-8"},{"key":"e_1_3_8_25_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-021-06389-1"},{"issue":"1","key":"e_1_3_8_26_2","first-page":"261","article-title":"Count data models for outpatient health services utilisation","volume":"22","author":"Abu Bakar NS","year":"2022","unstructured":"Abu Bakar NS, Ab Hamid J, Mohd Nor Sham MS, Jailani AS. Count data models for outpatient health services utilisation. BMC Health Serv Res. 2022;22(1):261.","journal-title":"BMC Health Serv Res"},{"key":"e_1_3_8_27_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0300257"},{"key":"e_1_3_8_28_2","doi-asserted-by":"publisher","DOI":"10.2307\/2344614"},{"key":"e_1_3_8_29_2","doi-asserted-by":"publisher","DOI":"10.12746\/swrccc.v3i9.191"},{"key":"e_1_3_8_30_2","volume-title":"Edps\/Psych\/Soc 589 Course Notes","author":"Anderson CJ","year":"2021","unstructured":"Anderson CJ. Statistical Inference & Model Checking for Poisson Regression. In: Edps\/Psych\/Soc 589 Course Notes. University of Illinois; 2021."},{"issue":"4","key":"e_1_3_8_31_2","first-page":"2068","article-title":"Poisson Regression Model for fertility count data and its applications","volume":"13","author":"Setti S","year":"2023","unstructured":"Setti S, Muniswamy B, Punyavathi B. Poisson Regression Model for fertility count data and its applications. J Chem Health Risks. 2023;13(4):2068-2078.","journal-title":"J Chem Health Risks"},{"key":"e_1_3_8_32_2","doi-asserted-by":"publisher","DOI":"10.18187\/pjsor.v21i1.3909"},{"issue":"7","key":"e_1_3_8_33_2","first-page":"949","article-title":"Supervised Machine Learning: A Comparison of Poisson and Negative Binomial Regression for Count Data Analysis","volume":"92","author":"Hamdi WA","year":"2025","unstructured":"Hamdi WA. Supervised Machine Learning: A Comparison of Poisson and Negative Binomial Regression for Count Data Analysis. Pushpa Publishing House. 2025;92(7):949-961.","journal-title":"Pushpa Publishing House"},{"key":"e_1_3_8_34_2","doi-asserted-by":"publisher","DOI":"10.1177\/1536867X1401400203"},{"key":"e_1_3_8_35_2","volume-title":"Negative Binomial Model. Department of Mathematics","author":"Famoye F","year":"2009","unstructured":"Famoye F. Negative Binomial Model. Department of Mathematics. Central Michigan University; 2009."},{"key":"e_1_3_8_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(07)61602-X"},{"key":"e_1_3_8_37_2","doi-asserted-by":"publisher","DOI":"10.1177\/20503121261426069"}],"container-title":["INQUIRY: The Journal of Health Care Organization, Provision, and Financing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00469580261486803","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/00469580261486803","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00469580261486803","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T03:48:16Z","timestamp":1788580096000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/00469580261486803"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":36,"alternative-id":["10.1177\/00469580261486803"],"URL":"https:\/\/doi.org\/10.1177\/00469580261486803","relation":{},"ISSN":["0046-9580","1945-7243"],"issn-type":[{"value":"0046-9580","type":"print"},{"value":"1945-7243","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]},"article-number":"00469580261486803"}}